International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Explaining real exchange rate behaviour
through tariffs: the BRIC’s case
Leonardo TARIFFI
Universitat de Barcelona;
IESE Business School
[email protected]
ABSTRACT
This paper aims to explain the importance of real variables and tariffs in the
structure of the behavior of real exchange rate in four emerging countries:
Brazil, Russia, India and China. Demonstrating the relevance of the BalassaSamuelson effect in the foreign exchange markets of the so-called BRIC
countries, this paper estimates both the long-run relationship, through
classical lineal stationary and cointegration techniques, and short-term
misalignment using an ordinary least squares method with error correction
mechanism.
KEY WORDS
real exchange rate, tariffs, purchasing power parity, exchange rate
misalignment, currency overvaluation, co-integration, unit roots test, error
correction models, the Ricardo - Samuelson - Balassa effect
JEL CODES
F31, F41, C22, C32
1. Introduction
Continuing fluctuations in the United States dollar exchange rate have increased the importance of
analyzing the evolution of the currency exchange market. In both the academic and managerial sectors,
the study of the exchange between different types of currencies is important because of its relationship
with the balance of payments crisis and its link with competitiveness and growth of countries.
Likewise, international transactions have been modified by the increasing participation of emerging
countries, not only in terms of real economy and world production, but also with regard to monetary
and foreign exchange markets.
The so-called BRIC countries (Brazil, Russia, India and China) are examples of trade and international
financial reorganization. These economies’ sustained economic growth is reflected in currency markets.
In Figure 1, it can be observed that BRIC’s time series of real exchange rate (RER) has changed over time.
To explain whether these changes are transitory or permanent is the first objective of this work.
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International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Having identified if there are permanent or transitory changes in the time series of the exchange rate, it
can be found the reasons underlying these variations. These permanent changes may be linked to the
real economy through structural mechanisms.
Figure 1: Real Exchage Rate
BRIC: Brazil, Russia, India and China
70.000000
60.000000
50.000000
40.000000
30.000000
20.000000
10.000000
0.000000
Q
1
1
Q 995
4
1
Q 99
3 5
1
Q 99
2 6
1
Q 99
1 7
1
Q 998
4
1
Q 99
3 8
1
Q 99
2 9
2
Q 000
1
2
Q 00
4 1
2
Q 00
3 1
2
Q 00
2 2
2
Q 003
1
2
Q 00
4 4
2
Q 00
3 4
2
Q 005
2
2
Q 00
1 6
2
Q 00
4 7
2
Q 00
3 7
20
08
14.00000
12.00000
10.00000
8.00000
6.00000
4.00000
2.00000
0.00000
Brazil
China
Russia
India
So urce: IFS/IM F - B LS - B DB - ROSSTA T - Reuters
The main reason to find real exchange rate determinants is that general macroeconomic time series,
such as terms of trade, government expenditure, net foreign assets, trade balance, productivity or tariffs
cause permanent changes in exchange rates and affect monetary and capital balances.
Moreover, there is a widely accepted consensus that trade and international transactions have been
favored by the reduction in import tariffs. International negotiations have had a positive influence on
tariffs reductions in products of most countries participating in the General Agreement on Tariffs and
Trade (GATT). From the Dillon Round to the Uruguay Round, different mechanisms and formulas to
change the level of tariffs consecutively have been proposed. Although the last meetings in the Doha
Round did not had the expected success in achieving the stated objectives of trade liberalization, the
previous agreements and bilateral negotiations between countries with different markets, growth rates
and degree of trade protection have resulted in a decrease in tariffs.
From the World Trade Organization’s (WTO) foundation until 2008, BRIC countries have reduced their
simple average of applied tariffs under the most favored nation principle by 43.68%, 19.44%; 66.39%
and 60.15%, respectively. This decrease in import tariffs could affect the real exchange rate in the same
way that it would affect a change in the aforementioned real variables (terms of trade, government
expenditure, net foreign assets and trade balance) or an improvement in technological innovations.
To show the effects of real determinants and tariffs on the real exchange rate equilibrium is the second
aim of this work.
This paper uses an empirical model to estimate real exchange rate in equilibrium (RERe) of the United
States dollar with respect to other currencies belonging to BRIC countries: the real, ruble, the rupee and
the yuan. It estimates the short-term relationship between the real exchange rate, real macroeconomic
variables and tariffs to assess if they are statistically significant. Cointegration techniques are applied to
find the long-term relationship, while the method of ordinary least squares (OLS) with error correction
mechanism (ECM) is used to determine the short-term relationship.
If changes in exchange rates of countries belonging to BRIC (see Figure 1) are permanent and explained
mainly by structural variables, the Balassa - Samuelson effect would be justified in this theoretical
framework. The main target of this work is to find the real determinants in both, in both the short and
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International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
long term, taking into account the Balassa - Samuelson effect and including tariffs as one of the
explanatory variables.
Finally, the exchange rates overvaluation is calculated to indicate whether there are economic policies
capable of compensating the underlying misalignments. This analysis is the ultimate goal outlined in this
investigation. Through discretionary variations of real determinants, in particular reductions in import
tariffs, policy makers can offset possible overvaluation.
This literature has been widely debated. Cassel introduced the concept of Purchasing Power Parity (PPP)
in 1918. Dornbusch (1976) analysed capital mobility in the Mundell (1964) and Fleming (1962) model
within an open economy to specify exchange rate determinants in the short term and international
transmissions of monetary disturbances in the long run. Edwards (b1988), based on Balassa (1964) and
Samuelson (1964), found other real determinants of the RER, and redefineds the theory of the
international economy equilibrium. In 1996, Obstfeld and Rogoff formalized the relationship between
RER and productivity through a dynamic partial equilibrium model which explains the Balassa Samuelson effect.
On the other hand, the United Nations Conference on Trade and Development (UNCTAD) and the WTO
provide information on consecutive decreases in tariff rates in their analysis and reports. Millet (2001)
and Messerlin (2006) analyzed changes in tariffs and their relation with international negotiation
rounds.
The empirical evidence related to this paper, such as that written by Frenkel (1981), Meese and Rogoff
(1988), Froot and Rogoff (1994) and Clarida and Galí (1994) shows how relevant monetary variables are
to explain exchange rate policies, but it is not conclusive about the levels or the signs of these changes
affecting RER behavior. Edwards (a1988) includes the tariff rate as a determinant of the real exchange
rate in his research but this influence is exerted through monetary mechanisms and not through the
Balassa - Samuelson effect.
The following chapters present an examination of the theoretical framework and the RER definition.
Secondly, after the presention of some facts about tariffs, the implemented methodology contrasting
the empirical evidence is specified. Finally, the results and conclusions are presented.
2. Theoretical Framework
There are two main approaches which explain RERe. The first approach is based on the Purchasing
Power Parity (PPP) theory studied by Cassel (1918) and the Mundell-Fleming model analysed by
Dornbusch (1976). This theory extends the IS-LM model, with free capital mobility and flexible prices in a
flexible exchange rate framework. Under these monetary assumptions, changes in the nominal
exchange rate are diminished by the domestic - foreign price relationship in between countries.
Monetary variations change prices. However, the nominal exchange rate reflects and opposes
international price changes in a way that ensures that the PPP or the RER is constant over time.
In the long-term , the Mundell - Fleming model, with flexible prices in a flexible exchange rate
framework, predicts that monetary expansion increases money supply, prices and nominal exchange
rates, but it does not affect real variables such that RER. Nominal exchange rate depreciation keeps the
purchasing power of domestic goods, with respect to foreign goods, in between the initial and the final
equilibrium points. This fact implies international price level equivalence, when it is measured as a
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International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
function of only one currency, value of money international equivalence, equilibrium stability in the PPP,
neutrality of the long run RERe changes and the money causality function.
Assuming perfect capital mobility and exchange rate overreaction, the balance of payments is in
equilibrium (BP=0) when the domestic interest rate is equal to the foreign interest rate (i=i*) (Tariffi,
2010). Domestic or foreign price changes are compensated by nominal exchange rate variations, so that
RER is constant in the long run. The model is the following:
RER = c1 * [(NER * PE) / PD]
(i)
Where NER is the nominal exchange rate, PE is external price for goods and services and PD is the
domestic price for goods and services. Assuming PPP:
NER = c2 * (PD / PE)
(ii)
Substituting (ii) in (i), obtains:
RERe = c1 * c2 = c3
(iii)
Any condition misalignment (iii) is temporary and is associated with transitory and speculative
deviations. The term c3 could be defined as equal to zero, equal to a constant which is different from
zero or equal to a constant plus a trend.
On the other hand, a second approach supported by Edwards (1988) shows that the RERe behaviour
cannot be solely explained by monetary variables, but also by real variables. RER changes are not
transitory. Fundamental real determinants cause permanent misalignments in the RERe. “The
equilibrium real exchange rate is thatrelative price of tradables to non-tradables that, for given
sustainable (equilibrium) values of other relevant variables such as taxes, international prices and
technology, results in the simultaneous attainment of internal and external equilibrium. Internal
equilibrium means that the non-tradable goods market clears in the current period, and is expected to
be in equilibrium in future periods. In this definition of equilibrium RER, it is implicit the idea that this
equilibrium takes place with unemployment at the “natural” level. External equilibrium, on the other
hand, is attained when the intertemporal budget constraint that states that the discounted sum of a
country’s current account has to be equal to zero, is satisfied. In other words, external equilibrium
means that the current account balances (current and future) are compatible with long run sustainable
capital flows” (Edwards 1988). This effect has been mathematically formulated by Maurice and Rogoff
(c1996) through a partial equilibrium dynamic model.
In the model, there are two countries with tradable and non-tradable goods with competitive labour
markets for each country. The tradable goods sector presents higher relative productivity, and workers’
mobility in between both productive tradable and non-tradable sectors is perfect. PPP is valid only for
tradable goods but non-tradable goods prices are different across countries. There is perfect mobility of
capital. Tradable and non-tradable production functions YT = ATF (KT,LT) and YNT = ANTF (KNT,LNT),
satisfying the following conditions:
A) Constant returns to scale in F(.). Multiplying each input K and L by λ, obtains: AF (λK, λL) → λAF (K, L)
for all λ > 0. Where K is capital, L is labour, A is technology and λ is a constant.
B) Positive and diminishing returns to private inputs. Calculating derivatives of F(.) with respect to each
input:
ӘF / ӘK = r > 0, Ә2F / ӘK2 < 0
ӘF / ӘL = w > 0, Ә2F / ӘL2 < 0
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April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Where r is the marginal product of capital and w is the marginal product of labour.
C) Inada condition. In the limit, the first derivatives of F (.) with respect to each input satisfying the
following conditions:
LimK→0 (ӘF / ӘK) = limL→0 (ӘF / ӘL) = ∞
LimK→∞ (ӘF / ӘK) = limL→∞ (ӘF / ӘL) = 0
Note that the marginal product of each input depends on the capital-labour ratio k=K/L. Moreover, Y =
AF (K,L) → Y = ALF (K/L,L/L) → Y = ALF(K/L,1) → Y = ALF(k,1) → Y = ALf(k) → Y = ALf(K/L).
ӘY / ӘK = ӘALf(K/L) / ӘK = A[Lf’(K/L) * (1/L)] = A(L/L)f’(K/L) = Af’(K/L) = Af’(k).
(1)
ӘY / ӘL = ӘALf(K/L) / ӘL = A[(1 * f(K/L)) + Lf’(K/L) * ((0*L-K*1)/L2) = A[f(K/L) + (L/L)f’(K/L) * (-K/L)] =
A[f(K/L) - f’(K/L) (K/L)] = A[f(k) - f’(k)k].
(2)
The firm maximization problem is the following:
Maximize profit () = t∞ (1 / (1+z))t [P * AF(K,L) - wL - rK], such that conditions A, B and C are satisfied.
Where z is the discount factor, P is the price of goods and services, w are the wages to workers, r is the
capital price and, it is assumed for simplicity, that capital depreciation is equal to zero.
Rewriting equations (1) and (2), first order conditions are the following:
Ә / ӘK = 0 → P * Af’(k) - r = 0 → r = P * Af’(k)
(3)
Ә / ӘL = 0 → P * A[f(k) - f’(k)k] - w = 0 → w = P * A[f(k) - f’(k)k]
(4)
Tradable goods sector:
r = PT * ATf’(k)
w = PT * AT[f(k) - f’(k)k]
Non-tradable goods sector:
r = PNT * ANTf’(k)
w = PNT * ANT[f(k) - f’(k)k]
Where T is tradable goods and NT are non-tradable goods. It is assumed that the level of prices is
defined in geometric averages with weights equal to γ and 1-γ for tradable goods prices and nontradable goods prices respectively.
PD = PDTγ * PDNT1-γ
(5)
PE = PETγ * PE’NT1-γ
(6)
Where PD is goods and services at domestic prices and PE is goods and services at foreign prices.
Taking into account the perfect mobility of labour in between both tradable and non-tradable
productive sectors, the following is obtained for each country:
PDT * ADT[d(k) - d’(k)k] = w = PDNT * ADNT[d(k) - d’(k)k]
(7)
PET * AET[g(k) - g’(k)k] = w = PENT * AENT[g(k) - g’(k)k]
(8)
Where D is the domestic country and E is the foreign country. Without losing generalization, tradable
goods prices can be equal to the numeraire (PTγ = P’Tγ = 1). Rewriting (7) and (8):
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International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
PDNT = ADT[d(k) - d’(k)k] / ADNT[d(k) - d’(k)k]
(7)
PENT = AET[g(k) - g’(k)k] / AENT[g(k) - g’(k)k]
(8)
Similarly, rewriting (5) and (6):
PD = (1)γ * PDNT1-γ = PDNT1-γ
(9)
PE = (1)γ * PENT1-γ = PENT1-γ
(10)
Real exchange rate is defined as: RER = c1 * [(NER * PE) / PD]. Using the PPP assumption in the tradable
goods market and substituting the numeraire: PT = NER * P’T → 1 = NER * 1 → NER = 1. Finally, RER = c1 *
[PE / PD]
(11)
Substituting (7) and (8) on (9), (10) and (11) the Balassa – Samuelson effect can be obtained:
RER = c1 *
AET[g(k) - g’(k)k] / AENT[g(k) - g’(k)k]
1-γ
-----------------------------------------------ADT[d(k) - d’(k)k] / ADNT[d(k) - d’(k)k]
In short, if an increase in tradable goods productivity, relative to non-tradable goods productivity, is
higher in the domestic economy than in the foreign economy, the RER decreases and has an
appreciation.
The Balassa - Samuelson theory is usually related to two assumptions: 1) Non-tradable goods prices
grow faster than tradable goods prices and, 2) the productivity growth rate of tradable goods relative to
the non-tradable goods productivity is higher in countries which are tradable goods are intensive.
According to this theory, the prices of non-tradable goods grow higher than tradable goods price growth
because the productivity growth rate of tradable goods is higher than the productivity growth rate of
tradable goods. If the growth rate of the productivity tradables- non-tradables ratio is higher in domestic
economies than in foreign economies, the domestic economy RER decreases or has an appreciation.
The nominal exchange rate offsets or compensates only changes in prices of tradable goods. Considering
a non-tradable sector intensive in labour, and a tradable sector intensive in capital, the Balassa Samuelson theory explains that domestic economic growth increases technological progress and
improves tradable goods productivity levels relative to non-tradable goods productivity. This
productivity improvement in the domestic economy, relative to the foreign economy, decreases the
RER.
The theory maintains that changes in nominal exchange rates do not necessarily oppose international
prices ratios to maintain RER constant over time. The core proposes a relationship between RERe and
the following real variables:
RERe = c4TT + c5PE + c6NFA + c7BT + c8TAR + c9PR
(iv)
Where TT is terms of trade, PE is government expenditure, NFA is net foreign assets, BT is trade balance
or openness level, TAR is tariffs and PR is productivity. There is a monetary variable in the lineal model
to test the interest rate differential or country risk influence over the RERe.
RERe real determinant signs are the following:
a) Terms of trade (TT): An increase in the international relative price of imports, as a proxy of TT, implies
the following effects over RER:
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ISSN: 2226-6348
a.1) Income effect: An increase in export prices (or a decrease in import prices) augments TT and
improves wages, encouraging goods and services consumption. This productivity growth in tradable
goods or an increase in non-tradable goods prices decreases or appreciates RER (negative relationship).
a.2) Substitutive effect: A decrease in export prices (or an increase in the import prices) decreases TT
and increases substitution of foreign goods by domestic goods. This productivity growth in tradable
goods or the increase in the price of non-tradable goods decreases or appreciates RER (positive
relationship).
The relationship TT and RER depends on the dominant effect.
b) Government expenditure (PE): The PE effect over the RER behaviour depends on the expenditure
composition in tradable or non-tradable goods. This effect also depends on how governmental
expenditure is financed as levels of investment, consumption and resources can be modified in the
private sector. An increase in public expenditure implies the following effects over RER:
b.1) Direct effect: An increase in the government demand of domestic goods and services incentivises
domestic production to grow. This productivity growth in tradable goods or an increase in the price of
non-tradable goods decreases or appreciates RER (negative relationship).
b.2) Indirect effect: If the increase in the government demand of domestic goods and services
overshoots private consumption, the productivity decrease in tradable goods or the decrease in the
price of non-tradable goods, will lead to an increase or depreciation of RER (positive relationship).
The relationship PE and RER depends on the difference between both the marginal domestic propensity
to consume in both the public and private sectors.(LEO this doesn’t make sense!!)
c) Net foreign assets (NFA): This variable is a measurement of the wealth of national agents in a foreign
currency. There are two transmission mechanisms to the RER:
c.1) Income effect: Any increase in net foreign assets increases wealth and domestic consumption levels.
This growth in the productivity of tradable goods, or an increase in the price of non-tradable goods,
decreases or appreciates RER (negative relationship).
c.2) Substitutive effect: A fall in net foreign assets decreases savings and investment levels and increases
domestic consumption. The productivity growth in tradable goods or the increase in non-tradable
goods prices decreases or appreciates RER (positive relationship).
The relationship NFA and RER depends on the dominant effect.
d) Trade balance (BT): This variable represents the level of trade openness. It is a measure of wealth of
national agents in a foreign currency. There are two transmission mechanisms to the RER:
d.1) Income effect: An increase in the level of trade openness (through diminishing tariffs or improving
international trade bureaucratic procedures) decreases international trade discretional distortions and
augments wealth, increasing private domestic consumption. This productivity growth in tradable goods
or the increase in non-tradable goods prices decreases or appreciates RER (negative relationship).
d.2) Substitutive effect: A decrease in the level of trade openness (through diminishing tariffs or
improving international trade bureaucratic procedures) decreases international trade discretional
distortions and augments wealth, increasing private foreign consumption. This productivity growth in
foreign tradable goods or increase in foreign non-tradable goods prices increases or depreciates RER
(positive relationship).
The relationship BT and RER depends on the dominant effect.
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International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
e) Tariffs (TAR): This variable represents the degree of trade openness of an economy. There are two
mechanisms of transmission to the RER:
e.1) Income effect: A decrease in tariffs improves the degree of trade openness, reducing discretionary
distortions in international trade, increasing wealth and private consumption of domestic goods and
services. An increase in the productivity of domestic tradable goods, relative to non-tradable goods (or
domestic non-tradables goods’ prices increases) decreases RER (positive relationship).
e.2) Substitution effect: A decrease in tariffs improves the degree of trade openness, reducing
discretionary distortions in international trade, increasing wealth and rising private consumption of
foreign goods and services. An increase in productivity of foreign tradable goods, relative to nontradable goods (or foreign non-tradable goods prices increases) increases RER (negative relationship).
The relationship TAR and RER depends on the dominant effect.
f) Productivity (PR): An increase in productivity implies an improvement in the production capacity in the
economy and allows for an increase in its level of economic activity. An increase in the productivity of
tradable goods or an increase in the price of non-tradable goods diminishes or appreciates the RER
(negative relationship).
3. Access to markets and tariffs
The General Agreement on Tariffs and Trade (GATT), established in 1947 and signed in 1948, developed
a set of international trade rules to permit and encourage tariff concessions between countries. Since its
inception and until the creation of the World Trade Organization (WTO), GATT’s eight rounds have taken
into account a schedule of meetings, a set of trade principles and specific methodologies during their
multilateral negotiations (see Table 1).
During the first five rounds (1947, 1949, 1951, 1956 and 1960), the regulation plan only included a
reduction in tariffs. These rounds were characterized by meetings between a small numbers of countries
negotiating each of the trading products. Since there were a large number of items, this level of detail
(product by product) was difficult to follow.
From the Kennedy Round to the Uruguay Round, negotiations began to include not only tariff reductions
but also other duties and trade barriers such as subsidies, property rights, textiles, agriculture, etc.
During the Kennedy Round (from 1964 to 1967) and after a major reform of U.S. domestic laws, it was
agreed that tariff reductions were calculated by using the linear formula, thereby protecting sensitive
products or exceptions to the general rule. During the Tokyo Round (from 1973 to 1979), it was
convenient to begin to apply the "Swiss" methodology, which balanced the effect of the first agreed
tariffs reductions in the lowest tariffs. In this round, sensitive products and agricultural products were
negotiated at different meetings.
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Table 1: Rounds of negotiations on trade and tariffs
General Agreement on Tariffs and Trade
Year
Place
Countries
Issues
Dillon Round
Kennedy Round
Tokyo Round
1947
1949
1951
1956
1960-1961
1964-1967
1973-1979
Geneva
Annecy
Torquay
Geneva
Geneva
Geneva
Geneva
23
13
38
26
26
62
102
Uruguay Round
1986-1994
Geneva
123
Tariffs
Tariffs
Tariffs
Tariffs
Tariffs
Tariffs and subsidies
Tariffis and other
trade barriers
Tariffis and other trade
barriers, property rights,
agriculture, textiles,
WTO's establishment.
Source: WTO
Finally, the Uruguay Round was the largest international trade negotiation and the most far-reaching
reform of the global trading system in terms of scale, not only because it was attended by 123
participating countries from 1986 to 1994, but also because negotiations covered almost all the issues
related with commerce and trade. During this round, mainly import duties on tropical products exported
by developing countries were reduced, rules for dispute settlement were reviewed and it was
established that members should report periodically about their commercial policy’s transparency.
Table 2 shows some of the results, sorted by type of country. If we take only industrial products into
account, the tariff agreements stipulated varying degrees of reductions depending on the type of
country and" five years of tariff reductions at a yearly rate of 20% after WTO’s foundation were agreed”
(Millet, 2001). On average, the tariffs of developed countries decreased by 40% those of developing
countries by 20% and those of countries in transition by 30%. Property rights increased in percentage
terms. Over the total amount of tariff lines, developing countries increased consolidated items from 15%
to 58% while those of countries in transition rose from 74% to 96%.
Table 2: The Uruaguay Round Tariff Agreements
Industrial
Products
Developed countries
Developing Countries
Countries in transition
Tariff reduction (%)
Before UR
After UR
Reduc.
6.8
15.3
8.6
3.8
12.3
6.0
40.0
20.0
30.0
Consolidated rates (%)
Before RU
After RU
94.0
15.0
74.0
99.0
58.0
96.0
Source: Millet 2001 according to World Bank's data
(UR) The Uruguay Round
The Uruguay Round ended with the enactment of the WTO’s foundation in 1995 and planned new aims
for the future. Some of these goals were consolidated in subsequent years and others were modified.
The timetable of the main issues in the negotiations can be observed in Table 3.
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ISSN: 2226-6348
Table 3: Uruguay Ronda's Built-in agenda
1996
- Maritime services: market access negotiations to end (30 June 1996, suspended to 2000,
now part of Doha Development Agenda)
- Services and environment: deadline for working party report (ministerial conference, December 1996)
- Government procurement of services: negotiations start
1997
- Basic telecoms: negotiations end (15 February)
- Financial services: negotiations end (30 December)
- Intellectual property, creating a multilateral system of notification and registration of geographical
indications for wines: negotiations start, now part of Doha Development Agenda
1998
- Textiles and clothing: new phase begins 1 January
- Services (emergency safeguards): results of negotiations on emergency safeguards to take effect
(by 1 January 1998, deadline now March 2004)
- Rules of origin: Work programme on harmonization of rules of origin to be completed (20 July 1998)
- Government procurement: further negotiations start, for improving rules and procedures (by end of 1998)
- Dispute settlement: full review of rules and procedures (to start by end of 1998)
1999
- Intellectual property: certain exceptions to patentability and protection of plant varieties: review starts
2000
- Agriculture: negotiations start, now part of Doha Development Agenda
- Services: new round of negotiations start, now part of Doha Development Agenda
- Tariff bindings: review of definition of “principle supplier” having negotiating rights under
GATT Art 28 on modifying bindings
- Intellectual property: first of two-yearly reviews of the implementation of the agreement
2002
- Textiles and clothing: new phase begins 1 January
2005
- Textiles and clothing: full integration into GATT and agreement expires 1 January
Source: WTO
According to the WTO, the following principles must be taken into account when carrying out tariff
reductions:
a) Freer trade: This principle was agreed on the basis of mutual concessions. The reductions were
arrived at gradually through negotiations between the parties concerned, thereby lowering trade
barriers and encouraging trade. The barriers concerned include customs duties and measures such as
import bans or quotas that restrict quantities selectively. Countries with lower tariffs have less
bargaining power.
b) Promoting fair competition: This principle is used when a country is a major provider of a product and
requests the opening of negotiations on that particular product. The WTO is described as a free trade
institution but the system does allow tariffs and other forms of protection. This principle can extend to
seconds tariff benefits to countries through the provision of unconditional MFN. That is, the conditions
that are applied to commercial nations have fewer restrictions should be applied to all other nations.
c) Encouraging reciprocity and economic reform: This principle established that tariff concessions must
be equivalent in terms of bilateral trade growth and mutual benefits. Although, this principle protects
countries with lower levels of development, bilateral agreements are influenced by the negotiating
capacity of countries. Developing countries need flexibility while the system’s agreements are
implemented. This fact allows for trade concessions for developing countries.
d) Predictability and consolidation: This principle was established to monitor the agreement upon
completion of the negotiations as arbitrated by the WTO through binding and transparency principles?.
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Moreover, promising not to raise a trade barrier can be as important as lowering one, because the
promise gives businesses a clearer view of their future opportunities. After countries agree to open their
markets, they are bound to their commitments. For goods, these commitments amount to ceilings on
customs tariff rates.
e) Most favored nation (MFN): This principle was introduced in the Kennedy Round. It says that under
the WTO agreements, countries cannot discriminate between their trading partners. To grant one
country a lower customs duty rate for one of their products means offering the same policy to all other
WTO members. Some exceptions are allowed and developing countries need not comply with the
principle of reciprocity.
Furthermore, tariff reductions were carried out using the following methodologies:
1) Product by product: This methodology involves the analysis of each of the decreasing tariffs or
customs duties. It means that the tariff reductions are discussed item by item.
2) Linear reductions: "The system’s idea is to agree on a certain percentage of tariff reduction to be
applied to all products (uniform). To avoid problems with sensitive products, the pure linear system is
altered to negotiate reductions by category of products (linear system for each product category) (Milet,
2001).”
3) "Swiss" formula: This methodology consists of a harmonizing formula that is effective at reducing
tariff peaks and tariff escalations. This means reductions in tariff ceilings with progressive reductions in
applied tariffs, taking into account a final tariff, an initial tariff and a reduction factor (item to be
negotiated).
4) Girard or WTO formula: It is a variant of the" Swiss" formula. This methodology consists of a
harmonizing formula with a specific coefficient that can be varied to reflect different levels of initial
tariffs.
5) "Capping" formula: This is a mechanism which sets groups of countries according to their ability to
adjust to the new tariff reductions. There are three categories of countries: leading countries (lowest
coefficient "1-a"), followers (moderate coefficient "1-a") and incoming countries (highest coefficient "1a").
After the WTO’s foundation, the system to reduce tariffs has changed significantly. The new
methodology is based on rounds of negotiations carried out in ministerial conferences. These
negotiations are characterized by a single contractual agreement, a unique dispute settlement body, a
new leadership, greater transparency and better performance.
Furthermore, the WTO specified a new regulation. First, there will be a unique new contract that sets all
negotiations, which will apply to every country member (except for multilateral agreements). Therefore,
developing countries cannot decide whether or not to sign an agreement. Second, a single arbitrage
body which blocks functional group of panels to solve disputes was established. Third, WTO will achieve
greater transparency and surveillance through the new Trade Policy Review Mechanism. Fourth, WTO’s
General Directors will be political figures instead officials appointed by the country members. Finally, it
would be different from the former regulation because it will be compulsory for signatory countries to
meet at least once every two years. Since the creation of the WTO, there have been the following
ministerial conferences:
Singapore (1996): A ministerial declaration was announced and 18 countries signed agreements on
trade of information technology products.
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Geneva (1998): A ministerial declaration and a general statement on electronic commerce were reannounced.
Seattle and the Millennium Round (1999): There were no statements issued at the end of this round.
Doha Round (2001): Several declarations and decisions known as the "Agenda for the Doha Round"
were announced.
Cancun (2003): No declaration was issued due to disagreements amongst developed countries about the
G-21’s position calling for the elimination of agricultural subsidies in developed countries.
Hong Kong (2005): A ministerial declaration and a list of questions for ministers which included 5 points
directly related to protectionism in agriculture and other 3 on non-agricultural products, were
announced.
Of the last ministerial conferences, the most relevant one proved to be the so-called Doha Round in
2001. This round aimed to continue and complete the goals set at the Uruguay Round, through the
implementation of agreements still current in 2001 and the improvement of agricultural trade
negotiations transparency.
Two different aspects characterized the Doha Round On the one hand; representatives of developing
economies (with large and growing markets) imposed high ceilings and tariffs, thus requiring greater
transparency in agricultural and commodities markets. On the other hand, representatives of developed
economies with low ceilings and tariff rates levels had only a narrow margin for negotiation.
In the Doha Declaration, there are 21 topics related to actual negotiations, implementation of
agreements, economic and political analysis and agreements’ surveillance. Major topics include issues
about agriculture, services, sanitary and phytosanitary measures, textiles and clothing sectors, access to
markets for non-agricultural products, measures on investment related to trade, antidumping and
subsidies, customs valuation, intellectual property rights, trade and competition policy, transparency in
government procurement, regional trade, dispute settlement, trade and environment, electronic
commerce, small economies and public finances.
The Doha Round did not have the expected output and country members decided to encourage tariff
reductions through bilateral negotiations. In this general scenario, negotiations may take place in the
Trade Negotiations Committee, in other WTO’s boards and committees or in external meetings. Some of
the G7 countries have negotiated tariff reductions directly with other markets, thus establishing bilateral
agreements with emerging economies such as those belonging to the BRIC countries.
In this context, Table 4 presents statistical data ad valorem under the harmonized system for the simple
average tariff rates, according to the principle of Most Favoured Nation (MFN) in the following
countries: Brazil, Russia, India and China. (Note that the source includes the harmonized system of Trade
Analysis and Information (TRAINS) through the WITS program of the United Nations Conference on
Trade and Development (UNCTAD), the Integrated Data Base (IDB) through the WITS program of the
World Trade Organization (WTO) and the World Bank.) Specifically, the statistics were obtained using a
simple average of applied import tariffs charged ad valorem at the national level line under the most
favoured nation principle. The statistics of some ome years have been completed using applied tariff
under 6 digits. Annual data have been quarterized with the time series of imports unit value.
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Table 4: Tariffs. Simple average ad valorem data from the harmonized system
Applied tariffs at national levels under the MFN principle*
Year
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Variation 96-08 (%)
Brazil
Russia
19.99
11.95
14.56
14.37
14.13
14.80
12.28
12.03
11.89
10.75
12.33
10.68
11.26
-43.68
India
10.89
12.63
13.90
12.60
11.10
10.26
9.81
n.d.
n.d.
9.65
n.d.
9.01
8.78
-19.44
China
37.00
30.09
n.d.
32.95
33.65
32.32
29.00
n.d.
29.94
18.86
15.05
17.08
12.43
-66.39
23.65
17.61
17.48
17.11
16.95
15.89
12.38
11.25
10.42
10.67
9.68
9.78
9.43
-60.15
Source: UNCTAD (TRAINS - WITS) / WTO (IDB - WITS) / World Bankl (siteresources.worldbank.org)
(*) Some years have been completed using applied tariffs under 6 digits. (n.d.) = no available
MFN = Most Favoured Nation
4. Methodology1
Basically, there are three econometric procedures. The first procedure performs the augmented Dickey Fuller test to find unit roots. The optimum lags order is calculated running the Schwarz information
criterion, and the critical values are based in MacKinnon to 1%, 5% and 10%. The presence of structural
breaks is tested through the Chow test. The second procedure is the Johansen method to test the
number of co-integration vectors under unrestricted intercepts and restricted tends assumptions.
Finally, the third procedure is an algorithm to minimize the sum of squared residuals of a lineal
regression under estimator efficient properties. The ordinary least squares (OLS) model includes the
stationary vectors found through using the cointegration procedure. The adjustment of the model to the
data taking into account the R2 is considered, as is the individual significance of the estimators, the
autocorrelation, the heteroskedasticity and the normality of the errors.
The econometric methodology is justified for two main reasons:
1) In order to test if nominal exchange rates are compensated by changes in domestic and foreign
prices, it is necessary to exam the RER behavior over time and to assess the validity of equation (iii)
presented in the theoretical framework: RERe = c1 * c2 = c3. An augmented Dickey - Fuller (1979) unit
roots test as a null hypothesis and MacKinnon (1991) critical values to 1, 5 and 10% to evaluate
stationarity in the RER (where parameters of the lagged variables are not statistically significant and
where the errors satisfy condition iid (0,2)) validates the equation (iii). Otherwise, no rejection of the
parameters statistical significance in the RER lagged would infer that there are determinants which
could explain the behavior of RER in levels.
2) In order to find if those determinants are linked to the RER through structural mechanisms, it is
convenient to introduce the Balassa - Samuelson effect in this theoretical framework, thereby showing
that RER changes are explained by real economy variables.
1
See Tariffi (2010) for further discussions.
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As described in the theoretical framework, an increase in A[i(k) - i’(k)k] (where i=g,d) in tradables
(relative to non-tradables) is higher in the domestic economy than in the foreign economy, the ERE
decreases. The transmission mechanism is as follows:
 (AET / AENT) =  (PDNT1-γ)   TCR (domestic currency increases)
 (ADT / ADNT) =  (PDNT1-γ)   TCR (domestic currency increases)
Those variables, which affect relative productivity between countries and between tradable and
nontradable sectors, will also affect relative prices between countries and between tradable and non
tradable sectors and will ultimately affect the RER. These determinants and their parameters signs are
presented in equation (iv) of the theoretical framework: RERe = c4TT + c5PE + c6NFA + c7BT + c8TAR +
c9PR. This relationship could exist in both the short or long term
An autorregresive vector (VAR) defined by Engle and Granger (1987) with the Johansen (1988, 1991)
methodology under unrestricted intercepts and restricted trends assumptions by Pesaran, Shin and
Smith (2000), evaluates the long run relationship of equation (iv) when a algorithm minimizing the sum
of squared residuals of a lineal regression under estimator efficient properties (OLS method with ECM)
determines the short-term relationship.
5. Empirical evidence
The applicability of the theoretical model is tied to the relationship between the U.S. dollar and
currencies belonging to BRIC countries: the real, ruble, rupee and yuan. It is followed by 4 steps to
develop the empirical work. First at all, the data is defined and calculated; second, the unit root time
series and the PPP theory are tested; third, the long- term run relationship between the cointegrated
variables to build the short term model with MCE is shown and; finally, the relationship between the
RER, tariffs and the other real determinants is estimated.
Data
The quarterly data starts in 1993:1 until 2008:4 for Brazil, India and China, and from 1995:1 to 2008:4 for
Russia (see tables 5, 6, 7 and 8 in annexes). This frequency of the data is due to the fact that most of the
"proxies" variables are published quarterly. For instance, one of the key "proxies" to explain RER
through real determinants and the Balassa - Samuelson effect is the Gross Domestic Product (GDP). The
GDP is published mostly on a quarterly basis.
The period of analysis is mainly limited by two facts:
a) There is no data available belonging to tariffs before the first year.
b) After the global financial crisis, the data showed a highly fluctuating pattern of behavior.
The base year data is 2005 and primary sources are the following:
1) The international finances statistics from the International Monetary Fund (IFS/IMF).
2) The macroeconomics data from the Statistical Office of the European Communities (Eurostat European Commission) and from the European Central Bank (ECB).
3) The inflation data from the Bureau of Labor Statistics of the United States (BLS).
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4) The Integrated Data Base (IDB) through the software World Integrated Trade Solution (WITS) from the
World Trade Organization (WTO).
5) The Trade Analysis Information System (TRAINS) through the software World Integrated Trade
Solution (WITS) from the United Nations Conference on Trade and Development (UNCTAD).
6) The Management System for Time Series of the Central Bank of Brazil (BDB).
7) The Federal State Statistics Service of Russia (Rosstat).
8) Reuters.
The proxy of the variables is the following:
Variable RER: The real exchange rate is calculated by multiplying the nominal exchange rate of each BRIC
country by its consumer price index respectively over the consumer price index of United States. RER =
NER*CPI/CPIUSA.
Variable TT: The terms of trade are obtained from the ratio unit value exports over unit value imports
divided by the gross domestic product for each country belongs to BRIC. TT = (UVX/UVI)/GDP.
Variable PE: The proxy of public expenditure is the current government expenditure for each BRIC’s
country dived by its consumer price index respectively. PE = PE/CPI.
Variable NFA: The net foreign assets are calculated dividing the international reserves plus gold for each
BRIC’s country, divided by the consumer price index of the United States. In the China case, foreign
assets replace the international reserve plus gold. NFA = IR+Gold/CPIUSA.
Variable BT: The balance of trade is the sum of the volume of exports plus the volume of imports divided
by the gross domestic product for each country belonging to BRIC. BT = (M+X)/GDP.
Variable TAR: Tariffs are obtained from the harmonized system, by calculating the simple average of the
applied custom rate at a national level of the ad valorem import tariff according to the principle of Most
Favoured Nation (MFN). Some years have been completed using applied tariff under 6 digits. Annual
data have been quarterized with the time series of imports unit value.
Variable PR: Productivity is obtained by dividing the gross domestic product for each BRIC country by its
number of full time employees respectively. PR = (GDP/Nº of Em.).
Rewriting equation (18) as the model (iv) in the theoretical framework:
RERe = f(t) + c4TT + c5PE + c6NFA + c7BT + c8TAR + c9PR + MCE + ut
(19)
Unit roots test
The unit roots tests contrast the purchasing parity power, observing if the real exchange rate deviations,
with respect to its equilibrium, are transitory or permanent.
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Figure 2: Real exchange rate - Brazil
Q
Q
1
19
93
1
19
Q 94
1
19
95
Q
1
19
Q 96
1
19
97
Q
1
19
Q 98
1
19
99
Q
1
20
Q 00
1
20
01
Q
1
20
Q 02
1
20
03
Q
1
20
Q 04
1
20
05
Q
1
20
Q 06
1
20
07
Q
1
20
08
3.50000
3.00000
2.50000
2.00000
1.50000
1.00000
0.50000
0.00000
RER = NER * CPI / CPIUSA
Figure 3: Real exchange rate - Russia
Q
1
1
Q 995
4
1
Q 995
3
19
Q 96
2
1
Q 997
1
1
Q 998
4
19
Q 98
3
1
Q 999
2
2
Q 000
1
20
Q 01
4
2
Q 001
3
2
Q 002
2
20
Q 03
1
2
Q 004
4
2
Q 004
3
20
Q 05
2
2
Q 006
1
2
Q 007
4
20
Q 07
3
20
08
40.00000
35.00000
30.00000
25.00000
20.00000
15.00000
10.00000
5.00000
0.00000
RER = NER * CPI / CPIUSA
Figure 4: Real exchange rate - India
Q
1
19
Q 93
1
19
Q 94
1
19
Q 95
1
19
96
Q
1
19
Q 97
1
19
Q 98
1
19
Q 99
1
20
Q 00
1
20
Q 01
1
20
Q 02
1
20
Q 03
1
20
Q 04
1
20
Q 05
1
20
Q 06
1
20
Q 07
1
20
08
70.00000
60.00000
50.00000
40.00000
30.00000
20.00000
10.00000
0.00000
RER = NER * CPI / CPIUSA
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Figure 5: Real exchange rate - China
Q
Q
1
19
93
1
19
94
Q
1
19
Q 95
1
19
Q 96
1
19
Q 97
1
19
Q 98
1
19
99
Q
1
20
Q 00
1
20
Q 01
1
20
Q 02
1
20
Q 03
1
20
04
Q
1
20
Q 05
1
20
Q 06
1
20
Q 07
1
20
08
16.00000
14.00000
12.00000
10.00000
8.00000
6.00000
4.00000
2.00000
0.00000
RER = NER * CPI / CPIUSA
Through the unit root Dickey - Fuller augmented test at 1, 5 and 10%, results can be obtained taking into
account data in levels, , in first difference and in second differences for the following cases: a) including
intercept, b) including intercept and trend and, c) not including intercept neither trend (see tables 9, 10,
11 and 12 in annexes). The optimal lag is calculated through the Schwartz information criterion. As the
data is quarterly, the maximum number of lags included in the tests is 9. Evaluating RER in levels for
each of the BRIC countries, a stationarity test shows that the null hypothesis of the unit root cannot be
rejected. Stationarity tests also show that all RER time series are integrated with order 1 or they are I(1),
with the only exception of Russia whose time series is integrated with order 2 or I(2). From figure 1 to 5,
it is evident that the behavior of the RER is not stationary in variance.
In order to evaluate stability in stationary models, a Chow test was applied. Breaking points correspond
to the dates where BRIC countries became members of the World Trade Organization (WTO). Brazil and
India signed the agreement in 1995 and China in 2001. In the case of Russia, the break point was set in
2005. Even though Russia only began negotiations to join as a member of the WTO in 1995, it signed
significant international agreements were during in the 27th Session of the Contracting Parties on April
15, 2005. During this year, the Russian Federation established negotiations on trade with 28 members of
the WTO and the European Community, thereby signing agreements about 87% of Russian imports.
Considering that there is no evidence of structural breaks in RER stationary models, the fact that the unit
root null hypothesis cannot be rejected justifies the next step in the methodological procedure: to find
empirical determinants along the lines of the model (19).
Table 9, 10, 11 and 12 also show optimal lag levels to assess whether determinats follow a path of
stationarity. In the Brasil model, TT, NFA, BT, TAR and PR are I(1) although variable PE is I(0). An
augmented Dickey Fuller test does not clearly evaluate stationarity without trends intercepting in the
case of TT and NFA. The Russia model presents TT, PE, BT and TAR integrated with order 1 although AEN
is I(0) and PR is I(2). Since results are not conclusive when stationarity of PR in first differences is
evaluated, stationarity of the time series PR in second differences has also been tested. In the case of
India, all variables are I(1) on the right hand of the equation. In first differences, the TT data is stationary
only when the intercept and the trend in the model is included, the NFA is weakly stationary, at 10%,
with intercept or intercept and trend in the regression and, the time series BT is not stationary when
there is no trend nor intercept. Finally in China, all the real determinants are I(1) despite NFA which is
I(0). The variable TT is stationary in levels when unit roots are evaluated without intercept or trend and
BT, TAR and PR have the same property with different levels of significance.
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Cointegration test
In order to find the long run relationship between variables RER, TT, PE, NFA, BT, TAR and PR for each
BRIC country, a VAR model of order 1 with unrestricted intercept and restricted trend was run. Tables
13, 14, 15 and 16 show the ratio of maximum likelihood with significance levels at 5% and 10% for each
country. In the case of Brazil, the variable PE is not included and there are two cointegrating vectors.
The trace test also allows three cointegrating vectors in Russia when NFA is not included in the model.
The inclusion of all real determinants resulted in four cointegrating vectors for India. And finally in
China, there are two cointegrating vectors excluding the NFA time series.
Moreover, table 17, 18, 19 and 20 (see annexes) present values of VAR estimators, respectively for each
country cointegrating vectors. Normalized values are in parentheses. All vectors found are used to
calculate error correction mechanisms.
Table 13: Brazil. Cointegration with unrestricted intercepts and restricted trends in the VAR
Cointegration LR Test Based on Trace of the Stochastic Matrix
*******************************************************************************
63 observations from 1993Q2 to 2008Q4. Order of VAR = 1.
List of variables included in the cointegrating vector:
RER
TT
NFA
BT
TAR
PR
Trend
List of eigenvalues in descending order:
.65756
.51727 .35311
.23554
.087576
.047107 .0000
*******************************************************************************
Null Alternative
Statistic
95% Critical Value 90% Critical Value
r=0
r>= 1
166.5728
115.8500
110.6000
r<= 1 r>= 2
99.0588
87.1700
82.8800
r<= 2 r>= 3
53.1766
63.0000
59.1600
r<= 3 r>= 4
25.7351
42.3400
39.3400
r<= 4 r>= 5
8.8139
25.7700
23.0800
r<= 5 r = 6
3.0399
12.3900
10.5500
*******************************************************************************
Use the above table to determine r (the number of cointegrating vectors).
Table 14: Russia. Cointegration with unrestricted intercepts and restricted trends in the VAR
Cointegration LR Test Based on Trace of the Stochastic Matrix
*******************************************************************************
54 observations from 1995Q3 to 2008Q4. Order of VAR = 1.
List of variables included in the cointegrating vector:
RER
TT
PE
BT
TAR
PR
Trend
List of eigenvalues in descending order:
.75049
.56635
.40603
.35360
.19516
.087990
.0000
*******************************************************************************
Null Alternative
Statistic
95% Critical Value 90% Critical Value
r=0
r>= 1
188.4741
115.8500
110.6000
r<= 1 r>= 2
113.5080
87.1700
82.8800
r<= 2 r>= 3
68.3901
63.0000
59.1600
r<= 3 r>= 4
40.2599
42.3400
39.3400
r<= 4 r>= 5
16.6975
25.7700
23.0800
r<= 5 r = 6
4.9736
12.3900
10.5500
*******************************************************************************
Use the above table to determine r (the number of cointegrating vectors).
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Table 15: India. Cointegration with unrestricted intercepts and restricted trends in the VAR
Cointegration LR Test Based on Trace of the Stochastic Matrix
*******************************************************************************
63 observations from 1993Q2 to 2008Q4. Order of VAR = 1.
List of variables included in the cointegrating vector:
RER
TT
PE
NFA
BT
TAR
PR
Trend
List of eigenvalues in descending order:
.75591
.64087
.52477
.46909
.21313 .14203
.10191
.0000
*******************************************************************************
Null Alternative
Statistic
95% Critical Value 90% Critical Value
r=0
r>= 1
271.6413
147.2700
141.8200
r<= 1 r>= 2
182.7979
115.8500
110.6000
r<= 2 r>= 3
118.2819
87.1700
82.8800
r<= 3 r>= 4
71.4126
63.0000
59.1600
r<= 4 r>= 5
31.5233
42.3400
39.3400
r<= 5 r>= 6
16.4224
25.7700
23.0800
r<= 6 r = 7
6.7713
12.3900
10.5500
*******************************************************************************
Use the above table to determine r (the number of cointegrating vectors).
Table 16: China. Cointegration with unrestricted intercepts and restricted trends in the VAR
Cointegration LR Test Based on Trace of the Stochastic Matrix
*******************************************************************************
63 observations from 1993Q2 to 2008Q4. Order of VAR = 1.
List of variables included in the cointegrating vector:
RER
TT
PE
BT
TAR
PR
Trend
List of eigenvalues in descending order:
.58274
.49416
.34506
.24274
.12370
.060307
0.000
*******************************************************************************
Null Alternative
Statistic
95% Critical Value 90% Critical Value
r=0
r>= 1
154.4179
115.8500
110.6000
r<= 1 r>= 2
99.3531
87.1700
82.8800
r<= 2 r>= 3
56.4168
63.0000
59.1600
r<= 3 r>= 4
29.7546
42.3400
39.3400
r<= 4 r>= 5
12.2375
25.7700
23.0800
r<= 5 r = 6
3.9187
12.3900
10.5500
*******************************************************************************
Use the above table to determine r (the number of cointegrating vectors).
Ordinary least squares (OLS) model with the error correction mechanism (ECM)
An OLS model with ECM determines the short-term relationship between the real exchange rate, tariffs
and the other key determinants. For each BRIC country, an interactive process with four distributed lags
finds the empirical model. Error correction mechanisms have been calculated from the cointegrated
vectors, lagged one period and included in the OLS models with a negative sign. Tables 21, 22, 23 and 24
show the results. Where, D stands for first difference and the negative number in parentheses is the lag
of the variable.
Table 21 shows the Brazil model. All variables are transformed into first differences except for public
expenditure, which is in levels. One of the two correction mechanisms is not found to be significant. R 2 is
greater than 50%. Variables lagged DBRATCR and DBRAAEN have a highly significant p-value associated
to its t-statistics. There is a significant seasonal variable (S2).
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Table 21: Dependent Variable: DBRARER
Method: Least Squares
Sample(adjusted): 1994:1 2008:4
Included observations: 60 after adjusting endpoints
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
DBRARER(-1)
DBRARER(-2)
DLBRATT(-1)
DLBRATT(-3)
DBRANFA(-2)
DBRABT(-3)
DBRATAR(-1)
DBRAPR
DBRAPR(-1)
ECM2
S2
2.930435
0.599910
-0.374113
-0.269030
0.245992
-0.000651
-0.259660
-0.019976
-483.9492
451.3190
-0.068363
0.148690
1.154277
0.131236
0.138092
0.150172
0.147025
0.000208
0.104511
0.010599
175.5078
180.6713
0.027222
0.086565
2.538763
4.571225
-2.709167
-1.791477
1.673130
-3.126947
-2.484533
-1.884709
-2.757422
2.498012
-2.511296
1.717669
0.0144
0.0000
0.0093
0.0795
0.1008
0.0030
0.0165
0.0655
0.0082
0.0160
0.0154
0.0923
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
20
0.519028
0.408806
0.137757
0.910898
40.49376
1.434014
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
0.041004
0.179163
-0.949792
-0.530923
4.708907
0.000074
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Table 22: Dependent Variable: D2RUSRER
Method: Least Squares
Sample(adjusted): 1996:3 2008:4
Included observations: 50 after adjusting endpoints
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
D2RUSRER(-1)
DLRUSTT(-1)
DLRUSTT(-2)
DRUSPE(-2)
DRUSPE(-3)
RUSNFA
RUSNFA(-1)
DRUSBT
DRUSBT(-2)
DRUSBT(-3)
DRUSBT(-4)
DRUSTAR
DRUSTAR(-2)
DRUSTAR(-4)
D2RUSPR
D2RUSPR(-2)
D2RUSPR(-3)
D2RUSPR(-4)
ECM1
ECM3
6.715707
0.240102
-1.647217
-0.926427
-0.651467
-0.596795
-0.003603
0.004033
-0.002096
-0.002406
-0.006931
-0.003717
0.184318
-0.151613
0.109493
-5785.601
-3858.774
-11293.94
-10822.24
-0.707913
-0.336739
0.669795
0.101935
0.556769
0.422403
0.127679
0.138187
0.000445
0.000469
0.001132
0.001306
0.001352
0.001273
0.037890
0.033857
0.035432
1582.146
760.7631
1188.658
1310.620
0.083832
0.119663
10.02651
2.355447
-2.958529
-2.193231
-5.102361
-4.318763
-8.093434
8.590763
-1.852465
-1.842045
-5.128323
-2.920600
4.864523
-4.478111
3.090215
-3.656805
-5.072241
-9.501424
-8.257344
-8.444465
-2.814070
0.0000
0.0255
0.0061
0.0365
0.0000
0.0002
0.0000
0.0000
0.0742
0.0757
0.0000
0.0067
0.0000
0.0001
0.0044
0.0010
0.0000
0.0000
0.0000
0.0000
0.0087
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.948630
0.913202
0.299845
2.607306
2.895708
1.539097
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
0.116497
1.017755
0.724172
1.527221
26.77661
0.000000
Table 23: Dependent Variable: DINDRER
Method: Least Squares
Sample(adjusted): 1994:2 2008:4
Included observations: 59 after adjusting endpoints
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
DLINDTT
DLINDTT(-1)
DINDPE(-4)
DINDNFA
DINDBT(-2)
DINDTAR(-2)
ECM3
40.22194
3.329282
-4.326488
0.705335
-0.009386
0.250251
0.114337
-0.161809
6.331071
1.278776
1.140941
0.159916
0.001606
0.088760
0.056290
0.026023
6.353102
2.603492
-3.792034
4.410646
-5.842807
2.819430
2.031223
-6.217929
0.0000
0.0121
0.0004
0.0001
0.0000
0.0068
0.0475
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
21
0.762306
0.729681
0.921641
43.32057
-74.60453
2.315283
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
0.627441
1.772654
2.800154
3.081854
23.36595
0.000000
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Table 24: Dependent Variable: DCHIRER
Method: Least Squares
Sample(adjusted): 1994:2 2008:4
Included observations: 59 after adjusting endpoints
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
DCHIRER(-1)
DCHIRER(-2)
DLCHITT(-2)
DCHIPE(-2)
CHINFA(-1)
CHINFA(-2)
CHINFA(-3)
CHINFA(-4)
DCHITAR
DCHITAR(-1)
DCHITAR(-2)
DCHIPR(-4)
ECM2
T
0.561278
0.051051
0.097075
0.547153
0.002608
-0.000230
0.000434
-0.000706
0.000510
0.034223
0.015010
0.020108
1.456317
-0.012343
0.007296
0.317317
0.019650
0.018343
0.265196
0.000915
7.53E-05
0.000144
0.000174
0.000117
0.006264
0.006259
0.006263
0.732205
0.004833
0.001584
1.768823
2.597990
5.292188
2.063206
2.849295
-3.051715
3.016183
-4.058740
4.374258
5.463712
2.398118
3.210698
1.988948
-2.554091
4.605611
0.0839
0.0127
0.0000
0.0450
0.0066
0.0038
0.0042
0.0002
0.0001
0.0000
0.0208
0.0025
0.0529
0.0142
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
Durbin-Watson stat
0.811775
0.751885
0.090927
0.363777
66.40077
2.081774
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
F-statistic
Prob(F-statistic)
-0.128502
0.182543
-1.742399
-1.214212
13.55449
0.000000
The short-term model of Russia is shown in Table 22. All variables are placed in first differences except
for RER and PR (in second differences) and net foreign assets (in levels). Only one error correction
mechanism is not significant. The R2 is relatively high (about 95%).
In the case of India, the model with all variables in first differences and four error correction
mechanisms was used.. Since three mechanisms are found to be insignificant, table 23 only presents
one. The measurement of R2 is about 76%. Finally, China’s model is presented in table 24. Net foreign
asset is the only time series introduced at levels. In between two error correction mechanisms, it is
found that ECM2 is significant. R2 is relatively high (about 81%). There is also a statistically significant
trend. Apart from the China model, all BRIC regressions include RER lags as a endogenous variable (right
hand of the equation), to capture the adjustments of present RER from speculative effects of past values
of RER. Error correction mechanisms coefficients are all negative and located between 0 and 1 as
predicted by the econometric theory. Note that variables TAR are statistically significant in all BRIC
models.
The Breusch - Godfrey serial correlation LM test is presented in table 25. Taking into consideration 2, 3
and 4 lags, the probability associated to the chi-square likelihood statistic test for each lag, the null
hypothesis of no autocorrelation for all autoregressive coefficients in the auxiliary regression models of
Brazil , India and China cannot be rejected.
22
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Table 25: Breusch-Godfrey Serial Correlation LM Test
Lag
2
3
4
Obs*R-squared
4.80
4.84
6.21
Prob. Chi-Square
0.09
0.18
0.18
Obs*R-squared
10.66
12.01
13.22
Prob. Chi-Square
0.00
0.01
0.01
Obs*R-squared
2.01
2.02
2.92
Prob. Chi-Square
0.37
0.57
0.57
Obs*R-squared
0.18
5.79
7.43
Prob. Chi-Square
0.91
0.12
0.11
Brazil
Russia
India
China
Source: Author's calculations
The White heteroskedasticity test without cross terms shows a statistic value equal to 24.20514,
40.63355, 23.769 and 29.01221; and a p-value associated with the statistic equivalent to 0.283, 0.487,
0.049 and 0.412 for the model of Brazil, Russia, India and China, respectively. Taking into account that
the associated p-values are larger than 1% (significance level), the null hypothesis of no
heteroskedasticity cannot be rejected. By using a Jarque - Bera test, normality in the residuals of the
models is assessed. There are the following associated p-values: Brazil (0,000), Russia (0,934), India
(0,180) and China (0,642). The null hypothesis of normal distribution in the residuals with a significance
level of α = 0.01, 0.05 and 0.10 cannot be rejected.
6. Results and conclusions
The unit root test infers non-stationarity on time series RER for each BRIC country and evaluates
empirically the PPP theory. This test examines stationary characteristics in real determinants presented
in tables 9, 10, 11 and 12. The cointegration test plotted 2 cointegrating vectors in Brazil model, 3
cointegrating vectors in Russia, 4 cointegrating vectors in India and 2 cointegrating vectors in China.
After finding long-term relations between the RER and its real determinants, short-term econometric
models with error correction mechanisms are estimated. The OLS with ECM methodology estimated
coefficients that are statistically significant.
Including 64 observations in the regressions of Brazil, India and China and 56 observations in Russia, the
models fit acceptably with the statistical data with R2 values corresponding to 0.52, 0.95, 0.76 and 0.81
for each BRIC countries, respectively. In figures 5, 6, 7 and 8, the solid line (RER variable in levels) and
the dashed line (regressed RER) fluctuate in unison.
In Brazil, variable tariff rates (TAR) are significant at 10%, and show a negative sign. With 1 lag, a
decrease in TAR increases the real exchange rate, depreciating the real-dollar relationship. With the
exception of productivity, all real determinants affect RER with 1, 2 and 3 lags. The joint effect from TT
to the RER is explained by the fact that domestic goods are replaced with imported goods after quarters
1 and 3. An increase in net foreign assets or the level of trade openness has a negative effect on RER
after 2 and 3 lags, respectively. These facts suggest a causality link. In the case of Russia, the variable
RER is affected by TAR in levels and when it is delayed 2 and 4 periods. The combined effect of the
23
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variable TAR on the RER is positive and highly significant, thereby indicating an appreciation of the rubledollar ratio when tariffs decreases. Note that, in opposition to the Brazil model, public expenditure is a
statistically significant variable in the Russia regression. After 2 and 3 periods, government demand
increases for goods and services in the domestic market stimulates the production level of tradable
goods at the domestic economy without displacing private consumption, thereby increasing the price of
non- tradable and decreasing the RER.
Figure 5: Short term model adjustment in Brazil
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
94
96
98
00
BRATCR
02
04
06
08
BRATCRE
Figure 6: Short term model adjustment in Russia
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40
30
20
10
0
1996
1998
2000
2002
RUSTCR
2004
2006
2008
RUSTCRE
Figure 7: Short term model adjustment in India
60
50
40
30
20
10
94
96
98
00
INDTCR
25
02
04
06
08
INDTCRE
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Figure 8: Short term model adjustment in China
15
14
13
12
11
10
9
8
7
6
94
96
98
00
CHITCR
02
04
06
08
CHITCRE
The short term regression in the Indian economy shows a positive relationship between TAR and RER. A
significant coefficient at 5 and 10% indicates that tariffs reductions are followed by an appreciation of
the rupee. The public expenditure effect over RER is positive and takes a year to become effective. After
4 quarters, the indirect effect dominates the direct effect and an expansionary fiscal policy depreciates
the RER. In China, the coefficients of the variable TAR both in levels and in lags are positive, not only
when they are evaluated individually but also when they are jointly tested. The NFA presents the highest
overall joint effect on the endogenous variable with 2 positive lags and 2 negative lags which offset each
other. Taking into account that the combined effect of variable TAR on the RER is positive, an increase in
foreign assets increases the level of foreign investment and decreases the RER.
A reduction in tariffs on imports adversely affects the RER in Brazil. In order to reduce the effect of the
currency depreciation on trade, it is necessary to compensate the rest of the real determinants through
discretionary changes. Apart from Brazil, the relationship between TAR and RER variable is positive in
the other BRIC countries. Reductions in tariffs in Russia, India and China, in international environments,
directly or indirectly modify economic international relations and such changes in reciprocal agreements
link rates and the real economy to fluctuations in the RER.
It is clear that BRIC international negotiations at the WTO have helped to reduce their tariffs, and apart
from Brazil, they have all positively affected the value of their currencies. Reductions in import tariffs in
Russia, India and China have decreased the RER. Taking into account the rigidity of the productivity
growth and changes in the underlying real exchange rate, it can be affirmed that only discretionary
changes in variables such as public expenditure, balance of trade and terms of trade, are able to balance
the equilibrium of the national currencies caused by decreases in tariffs.
26
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7. Bibliografía:
Balassa, Bela. (1964) The Purchasing-Power Parity Doctrine: A Reappraisal, The Journal of Political
Economy, 72(6), Diciembre, pp. 584-596.
Carrion-i-Silvestre, J. Ll., del Barrio, T. y López-Bazo, E. (2004). Evidence on the Purchasing Power Parity
in a Panel of Cities, Applied Economics, 36(9), pp. 961-966.
Carrion-i-Silvestre, J. Ll. y Basher, Syed; Price level convergence, purchasing power parity and multiple
structural breaks in panel data analysis: An application to U.S. cities, Journal of Time Series
Econometrics, manuscript submitted 1000.
Cassel, Gustav. (1918). Abnormal deviation in international exchanges, Economic Journal, 28(112),
Diciembre, pp. 413-415.
Clarida, Richard y Galí, Jordi. (1994). Sources of real exchange rate fluctuations: How important are
nominal shocks?, National Bureau of Economic Research, Working Paper No. 4658, Febrero.
Conferencia de las Naciones Unidas para el Comercio y el Desarrollo; UNCTAD handbook of statistics,
United Nations Publications, 2008.
Dickey, David y Fuller, Wayne. (1979). Distribution of the estimators for autoregressive time series with a
unit root, Journal of the American Statistical Association, 74(366), Junio, pp. 427-431.
Dickey, David y Fuller, Wayne. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a
Unit Root, Econometrica, 49(4), Julio, pp. 1057-1072.
Dornbusch, Rudiger. (1976). La Teoría de los Regímenes de Tipos de Cambio Flexibles y la Política
Macroeconómica, Cuadernos de Economía (Latin American Journal of Economics), 13(39), pp. 27-50.
Edwards, Sebastian; Real and Monetary Determinants of Real Exchange Rate Behavior: Theory and
Evidence from Developing Countries, UCLA Working Paper No. 506, Septiembre a1988a.
Edwards, Sebastian; The determination of equilibrium real exchange rate, UCLA Working Paper No. 508,
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National Bureau of Economic Research, Working Paper No. 4952.
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Control, 12(2-3), Junio-Septiembre, pp. 199-607.
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MacKinnon, James. (1990). Critical Values for Cointegration Tests, University of California at San Diego,
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8. Annexes. Table 5:
Table 5: Data Brazil (Levels)
Index 2005=100
Year
RER
TT
NER*CPI/CPIUSA (UVX/UVI)/GDP
Q1 1993
Q2 1993
Q3 1993
Q4 1993
Q1 1994
Q2 1994
Q3 1994
Q4 1994
Q1 1995
Q2 1995
Q3 1995
Q4 1995
Q1 1996
Q2 1996
Q3 1996
Q4 1996
Q1 1997
Q2 1997
Q3 1997
Q4 1997
Q1 1998
Q2 1998
Q3 1998
Q4 1998
Q1 1999
Q2 1999
Q3 1999
Q4 1999
Q1 2000
Q2 2000
Q3 2000
Q4 2000
Q1 2001
Q2 2001
Q3 2001
Q4 2001
Q1 2002
Q2 2002
Q3 2002
Q4 2002
Q1 2003
Q2 2003
Q3 2003
Q4 2003
Q1 2004
Q2 2004
Q3 2004
Q4 2004
Q1 2005
Q2 2005
Q3 2005
Q4 2005
Q1 2006
Q2 2006
Q3 2006
Q4 2006
Q1 2007
Q2 2007
Q3 2007
Q4 2007
Q1 2008
Q2 2008
Q3 2008
Q4 2008
0.00002
0.00010
0.00052
0.00316
0.02304
0.19802
0.44236
0.44394
0.46816
0.52218
0.57136
0.60707
0.63145
0.65657
0.68123
0.69598
0.71727
0.74188
0.75666
0.77381
0.79496
0.81361
0.82424
0.83764
1.25572
1.22996
1.34461
1.41431
1.30765
1.32810
1.36467
1.46515
1.53326
1.74858
1.99114
2.04774
1.92180
2.04156
2.59124
3.18668
3.16355
2.78503
2.73695
2.75371
2.75271
2.90157
2.89416
2.74692
2.63879
2.48613
2.31059
2.27687
2.21922
2.18842
2.17852
2.18956
2.13465
1.99629
1.94595
1.81940
1.76910
1.67627
1.71478
2.46339
0.02633
0.02283
0.02372
0.02378
0.02454
0.02186
0.01807
0.01602
0.01396
0.01363
0.01243
0.01557
0.01357
0.01332
0.01322
0.01329
0.00936
0.01261
0.01181
0.01321
0.01094
0.01167
0.01140
0.01094
0.01301
0.01189
0.01014
0.01194
0.01184
0.01101
0.01115
0.01194
0.00998
0.01046
0.01124
0.01194
0.01239
0.01302
0.00977
0.01275
0.01167
0.01265
0.01413
0.01337
0.01050
0.01347
0.01113
0.01164
0.01082
0.01020
0.01014
0.00949
0.00938
0.00886
0.00907
0.01004
0.00928
0.00862
0.00783
0.00794
0.00787
0.00669
0.00650
0.00725
PE
NFA
BT
TAR
PR
PE/CPI
IR+Gold/CPIUSA
(M+X)/GDP
Applied MFN
(GDP/Nº of Em.).
296.54263
323.98511
355.88925
424.98160
503.93421
562.07206
564.52813
502.36465
431.47908
425.24422
616.33777
655.25831
695.18225
743.74203
722.71749
735.57012
715.32521
697.38540
745.38676
626.65495
820.39569
843.90023
541.36788
523.82222
400.40657
485.82401
495.33027
421.94209
447.41007
320.35989
353.57309
370.71551
381.56341
409.66269
438.95684
396.62114
401.30184
456.17230
414.35015
408.69775
449.09956
510.10845
555.76663
522.65217
538.15594
513.01299
509.29748
543.53524
626.32698
601.61778
560.33024
534.16557
585.07053
603.53882
706.79991
831.16538
1042.23582
1379.56330
1527.31080
1677.67496
1786.57084
1793.37265
1844.24768
1801.15603
1.6428
1.6805
2.0728
1.8537
1.6572
1.8972
1.7584
1.9463
1.8961
1.8864
1.7643
1.8898
1.6191
1.8241
1.8771
1.9825
1.3768
1.9163
1.8453
1.9142
1.9497
1.8783
1.9264
1.7847
1.6392
1.8855
1.8043
1.8682
1.7634
1.7955
1.9692
1.9210
1.8649
1.8963
2.0350
1.9123
1.7731
1.6294
2.3584
1.9016
1.8936
1.9313
2.1118
2.0751
2.1294
1.9101
2.3665
2.1288
1.9494
1.9574
2.1562
2.0210
2.0085
1.7757
2.4143
2.1978
2.1463
2.1196
2.4098
2.2746
2.0547
2.3277
2.3691
2.0304
13.94452
14.45101
12.31070
14.40891
11.95406
12.55900
13.41097
13.42457
17.26741
19.38358
17.80852
14.58318
19.98736
19.69844
19.52187
20.68197
11.94692
9.31505
12.93021
10.71399
14.56454
14.45288
14.47885
15.41242
14.37022
13.88495
16.12240
13.63022
14.13190
15.04267
14.02748
13.42136
14.80000
14.06996
13.21895
13.87197
12.28011
13.09183
11.63527
12.02497
12.03113
11.66564
11.23595
13.12536
11.89170
12.61680
11.96662
13.12613
10.75179
11.59778
11.13783
11.42112
12.32982
14.47466
10.65204
12.23260
10.67825
11.71721
11.17238
11.53011
11.25630
12.74590
12.84016
10.61272
0.004523
0.004659
0.004922
0.004898
0.004461
0.004596
0.004778
0.004737
0.004305
0.004786
0.005048
0.004972
0.004688
0.004834
0.004952
0.004753
0.004499
0.004807
0.004861
0.004893
0.004609
0.004852
0.004979
0.005035
0.004784
0.004956
0.005019
0.004875
0.004743
0.004854
0.004865
0.004760
0.004564
0.004788
0.004872
0.004953
0.004584
0.004874
0.004920
0.004778
0.004728
0.004909
0.004927
0.004869
0.004896
0.005132
0.004985
0.004903
0.004867
0.005099
0.005018
0.004968
0.004965
0.005146
0.005116
0.005057
0.005189
0.005398
0.005360
0.005324
0.005339
0.005510
0.005544
0.005235
281.3439
145.2940
68.5662
29.9935
8.5231
3.0048
2.2537
2.4816
1.8566
1.7451
1.7514
1.9134
1.4599
1.5070
1.6565
1.5368
1.3588
1.3874
1.4753
1.6129
1.3203
1.3713
1.5054
1.6424
1.3072
1.3464
1.4311
1.5693
1.2664
1.2856
1.2885
1.4377
1.2137
1.2331
1.2435
1.3842
1.1935
1.2078
1.2103
1.2900
1.0300
1.0376
1.0627
1.1925
0.9877
1.0283
1.0458
1.1581
0.9676
0.9808
0.9963
1.0967
0.9542
0.9662
0.9818
1.0784
0.9700
0.9944
0.9865
1.0713
0.9874
0.9823
0.9878
1.0639
Source: IFS/IMF - BLS - WTO/TRAINS - BDB
29
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International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Table 6:
Table 6: Data Russia (Levels)
Index 2005=100
Year
RER
TT
NER*CPI/CPIUSA (UVX/UVI)/GDP
Q1 1995
Q2 1995
Q3 1995
Q4 1995
Q1 1996
Q2 1996
Q3 1996
Q4 1996
Q1 1997
Q2 1997
Q3 1997
Q4 1997
Q1 1998
Q2 1998
Q3 1998
Q4 1998
Q1 1999
Q2 1999
Q3 1999
Q4 1999
Q1 2000
Q2 2000
Q3 2000
Q4 2000
Q1 2001
Q2 2001
Q3 2001
Q4 2001
Q1 2002
Q2 2002
Q3 2002
Q4 2002
Q1 2003
Q2 2003
Q3 2003
Q4 2003
Q1 2004
Q2 2004
Q3 2004
Q4 2004
Q1 2005
Q2 2005
Q3 2005
Q4 2005
Q1 2006
Q2 2006
Q3 2006
Q4 2006
Q1 2007
Q2 2007
Q3 2007
Q4 2007
Q1 2008
Q2 2008
Q3 2008
Q4 2008
0.41388
0.60687
0.64383
0.74508
0.85156
0.94293
1.01039
1.07929
1.16399
1.22579
1.25134
1.27857
1.34265
1.37948
2.37912
6.32705
10.12615
11.68216
12.44628
13.67147
15.21691
15.63435
15.99226
16.80793
18.11800
19.24234
19.88410
20.99536
22.68000
23.72823
24.23601
25.14702
25.95755
26.15470
25.92951
26.05604
25.56010
26.12283
26.90394
26.96275
27.22580
28.16221
28.30027
29.19754
29.53440
28.61912
28.52936
28.76797
28.91470
28.59923
28.76989
28.53793
28.94765
28.57836
29.96104
35.87728
0.01149
0.01110
0.00880
0.00889
0.01132
0.01128
0.01001
0.01222
0.01081
0.00941
0.00793
0.00843
0.01101
0.01147
0.01410
0.02101
0.01434
0.01345
0.01352
0.01608
0.01501
0.01466
0.01227
0.01182
0.01244
0.01002
0.00857
0.00752
0.01129
0.01106
0.00851
0.00902
0.01043
0.00876
0.00788
0.00760
0.01024
0.00955
0.00873
0.00835
0.01013
0.00968
0.00846
0.00772
0.00927
0.00765
0.00642
0.00522
0.00662
0.00570
0.00488
0.00491
0.00665
0.00561
0.00495
0.00412
PE
NFA
BT
TAR
PR
PE/CPI
IR+Gold/CPIUSA
(M+X)/GDP
Applied MFN
(GDP/Nº of Em.).
84.55670
159.04791
173.73636
219.03767
241.55556
197.97873
186.67387
188.79016
201.44641
299.24236
280.00868
215.43547
203.09433
193.82961
151.79415
145.72119
127.48192
142.88549
130.48484
144.61144
177.27528
237.97657
281.31075
314.12347
329.46192
384.79090
415.97186
404.97894
407.57293
473.33613
492.48249
516.23729
589.01113
685.33173
654.92446
815.72131
869.58631
908.77275
978.35720
1278.79083
1388.73196
1522.79791
1568.30615
1809.43577
2013.47866
2412.98776
2563.56807
2940.99658
3224.09239
3806.10975
3986.70827
4454.00195
4690.67158
5080.82417
4973.02168
3969.58091
496.1516
531.5217
462.8715
576.2959
634.3090
651.0993
572.5550
678.7929
621.2802
636.0501
575.8225
707.2175
634.9215
617.2071
493.9360
473.3147
434.3075
443.4398
396.9873
512.9625
530.5736
521.2406
478.1430
575.7840
544.7206
560.2940
464.7662
520.6845
490.0275
554.9702
512.2390
588.7275
622.5955
619.9212
595.0280
664.9282
708.1535
760.6722
746.1801
862.1027
890.2903
961.4795
930.7828
1024.1371
1079.5493
1159.1503
1089.7356
1171.0552
1175.4279
1275.9421
1224.6934
1454.5742
1605.7469
1752.9423
1722.9889
1375.5261
9.56332
10.40858
10.31226
11.40777
10.89358
11.41617
10.81985
11.21156
12.62736
14.22427
13.54003
14.52065
13.90000
13.54700
10.73682
9.68056
12.60000
13.97815
11.82259
14.39251
11.10000
11.54345
11.90050
13.34217
10.25651
12.36754
9.97243
12.10139
9.81303
12.49789
11.31069
9.81303
12.48910
14.22277
13.43126
14.23798
7.90250
9.11494
8.58634
9.34728
9.65243
11.04835
10.64561
11.25196
6.49926
8.17201
7.13660
7.92433
9.00540
11.10769
9.95830
10.74355
8.77593
10.99504
9.64375
7.75963
0.000909
0.000943
0.001094
0.001001
0.000895
0.000917
0.001036
0.000969
0.000886
0.000901
0.001055
0.001088
0.000951
0.000968
0.001056
0.001010
0.000950
0.001002
0.001172
0.001114
0.001045
0.001076
0.001249
0.001174
0.001068
0.001107
0.001305
0.001214
0.001089
0.001146
0.001343
0.001285
0.001180
0.001246
0.001413
0.001405
0.001260
0.001337
0.001508
0.001507
0.001301
0.001398
0.001580
0.001584
0.001370
0.001499
0.001682
0.001697
0.001456
0.001573
0.001756
0.001808
0.001572
0.001672
0.001850
0.001838
6.7514
6.6346
6.5742
6.5332
6.4289
6.4175
6.4242
6.4241
7.0620
7.0595
7.0559
7.0569
5.5680
5.5672
5.5260
5.4512
4.2457
4.2392
4.2383
4.2399
5.4473
5.4519
5.6597
5.4554
6.0431
6.0407
6.0438
6.0438
6.7831
6.7945
6.7976
6.7959
7.2111
7.2773
7.2775
7.3483
7.9972
8.0082
7.9950
8.0829
8.8357
8.9215
9.0035
9.1376
10.3427
10.4812
10.4918
10.5223
11.8065
11.9462
11.9085
12.0287
12.6399
12.7827
12.7827
12.9151
Source: IFS/IMF - BLS - WTO/TRAINS - ROSSTAT
30
www.hrmars.com
International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Table 7:
Table 7: Data India (Levels)
Index 2005=100
Year
RER
TT
NER*CPI/CPIUSA (UVX/UVI)/GDP
Q1 1993
Q2 1993
Q3 1993
Q4 1993
Q1 1994
Q2 1994
Q3 1994
Q4 1994
Q1 1995
Q2 1995
Q3 1995
Q4 1995
Q1 1996
Q2 1996
Q3 1996
Q4 1996
Q1 1997
Q2 1997
Q3 1997
Q4 1997
Q1 1998
Q2 1998
Q3 1998
Q4 1998
Q1 1999
Q2 1999
Q3 1999
Q4 1999
Q1 2000
Q2 2000
Q3 2000
Q4 2000
Q1 2001
Q2 2001
Q3 2001
Q4 2001
Q1 2002
Q2 2002
Q3 2002
Q4 2002
Q1 2003
Q2 2003
Q3 2003
Q4 2003
Q1 2004
Q2 2004
Q3 2004
Q4 2004
Q1 2005
Q2 2005
Q3 2005
Q4 2005
Q1 2006
Q2 2006
Q3 2006
Q4 2006
Q1 2007
Q2 2007
Q3 2007
Q4 2007
Q1 2008
Q2 2008
Q3 2008
Q4 2008
17.13240
19.54613
20.17732
20.68107
20.58851
21.06922
21.76507
22.13777
22.04864
22.55126
24.04394
26.32944
26.40067
26.53523
28.16466
28.61503
28.63853
28.80544
29.27560
31.19020
33.70567
35.58121
39.35895
40.89112
39.06565
39.31341
40.20066
40.96787
40.06226
41.03127
42.35507
43.79927
42.80296
43.41874
44.94217
46.32482
46.26434
46.86196
47.30946
47.36373
45.82296
46.19658
45.24673
45.26936
44.36294
43.82941
46.13737
45.13379
43.30390
43.17981
43.26502
46.27750
44.47752
45.73167
47.78945
47.61351
46.06576
42.93769
43.36972
42.34276
42.44690
44.50414
48.68682
57.60750
0.02257
0.02099
0.01973
0.01809
0.02112
0.01997
0.01845
0.01765
0.01994
0.01917
0.01706
0.01570
0.01775
0.01591
0.01435
0.01489
0.01700
0.01893
0.01508
0.01436
0.01556
0.01662
0.01296
0.01226
0.01406
0.01514
0.01222
0.01190
0.01348
0.01432
0.01203
0.01223
0.01351
0.01399
0.01130
0.01128
0.01239
0.01297
0.01097
0.01069
0.01184
0.01187
0.01004
0.01023
0.01114
0.01145
0.00972
0.00969
0.00830
0.00946
0.00786
0.00893
0.01047
0.00907
0.00982
0.00887
0.00717
0.00890
0.00912
0.00834
0.01246
0.01290
0.01140
0.01128
PE
NFA
BT
TAR
PR
PE/CPI
IR+Gold/CPIUSA
(M+X)/GDP
Applied MFN
(GDP/Nº of Em.).
131.96739
139.05687
153.11536
181.24998
249.92098
264.66309
295.35351
300.90634
320.11915
304.05521
292.02198
274.84875
266.63215
271.07417
278.71792
293.02337
318.13172
353.53150
353.30175
333.96392
346.43733
320.90398
345.11617
355.66472
386.18856
394.08488
387.89391
407.16275
435.41865
417.59870
399.75971
450.93087
470.04468
478.21592
491.51067
533.00498
591.93097
630.25580
679.55359
760.18118
802.14490
878.14451
964.99968
1084.19667
1171.25669
1222.35668
1222.24151
1338.95771
1421.00652
1380.12085
1397.65276
1350.58057
1472.40717
1566.18445
1581.02137
1705.19790
1883.47095
1990.28619
2309.43859
2547.75696
2817.43432
2771.20510
2539.99860
2361.07162
6.8442
6.7125
6.4411
6.1750
7.9325
6.9324
7.3047
7.6681
9.2485
8.8928
8.7786
9.2973
11.4843
9.6326
8.7038
9.5395
12.6195
12.6028
10.3143
10.7141
13.1620
13.1611
12.0064
11.1226
12.7965
13.5621
12.6690
12.7745
14.9192
15.8946
14.0661
14.4660
15.5893
16.1113
13.8943
13.4874
16.0293
17.6082
16.2169
15.8866
18.2355
18.0438
15.6985
18.4798
21.4669
20.9185
20.6430
22.6245
28.3772
28.6802
25.3037
25.2351
30.5167
31.5790
31.0932
29.0119
31.5829
34.3124
29.6275
30.3591
38.3528
44.1334
43.0793
37.5740
47.80000
48.78756
49.54170
49.23146
47.80000
49.18285
49.84531
48.69658
41.00000
42.31501
43.00228
41.52059
37.00000
38.36315
38.54013
37.68519
30.09231
30.37864
30.57446
30.79018
30.22866
30.88808
32.11311
31.58944
32.94551
33.07742
33.60231
33.64100
33.65109
34.30075
33.95698
33.97953
32.32250
32.88022
33.22826
32.55437
29.00364
29.33486
29.67908
29.20367
28.78789
29.38187
29.07886
28.97994
29.93625
30.17382
30.68270
30.12791
18.86237
19.34230
20.21165
16.78089
15.04521
19.12312
12.59414
16.09259
17.07948
13.96870
14.50278
17.74412
12.43390
12.77078
12.94075
12.69173
0.001707
0.002053
0.002147
0.002187
0.001817
0.002089
0.002168
0.002113
0.001940
0.002018
0.002177
0.002340
0.002064
0.002435
0.002662
0.002475
0.002030
0.001846
0.002401
0.002433
0.002131
0.002130
0.002642
0.002616
0.002278
0.002246
0.002738
0.002626
0.002414
0.002507
0.002970
0.002843
0.002524
0.002577
0.003122
0.003002
0.002714
0.002761
0.003171
0.003120
0.002724
0.002892
0.003289
0.003102
0.002716
0.002862
0.003226
0.003103
0.002766
0.002829
0.003250
0.003198
0.002782
0.002828
0.003208
0.003163
0.002693
0.002799
0.003200
0.003157
0.002740
0.002858
0.003230
0.003106
5.5783
4.8721
4.8256
4.9668
5.3911
4.8628
4.7824
5.0257
5.8530
5.3738
5.2721
5.4533
6.5690
5.8922
5.6402
5.6628
5.6908
5.1067
6.2559
6.3441
6.3871
5.9056
6.8108
6.6651
6.9347
6.4617
7.7880
7.7797
7.2556
6.7564
8.0220
7.9231
7.3265
6.9128
8.1513
8.0682
7.2260
6.8545
7.8960
8.0119
7.4155
7.2393
8.4359
8.2664
7.7203
7.5454
8.6914
8.6448
7.2246
9.1634
9.7218
11.1033
10.5556
8.1139
9.5552
11.7343
10.1878
8.9019
9.5834
13.6103
11.3647
9.7274
12.0624
16.0789
Source: IFS/IMF - BLS - WTO/TRAINS
31
www.hrmars.com
International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Table 8:
Table 8: Data China (Levels)
Index 2005=100
Year
RER
TT
NER*CPI/CPIUSA (UVX/UVI)/GDP
Q1 1993
Q2 1993
Q3 1993
Q4 1993
Q1 1994
Q2 1994
Q3 1994
Q4 1994
Q1 1995
Q2 1995
Q3 1995
Q4 1995
Q1 1996
Q2 1996
Q3 1996
Q4 1996
Q1 1997
Q2 1997
Q3 1997
Q4 1997
Q1 1998
Q2 1998
Q3 1998
Q4 1998
Q1 1999
Q2 1999
Q3 1999
Q4 1999
Q1 2000
Q2 2000
Q3 2000
Q4 2000
Q1 2001
Q2 2001
Q3 2001
Q4 2001
Q1 2002
Q2 2002
Q3 2002
Q4 2002
Q1 2003
Q2 2003
Q3 2003
Q4 2003
Q1 2004
Q2 2004
Q3 2004
Q4 2004
Q1 2005
Q2 2005
Q3 2005
Q4 2005
Q1 2006
Q2 2006
Q3 2006
Q4 2006
Q1 2007
Q2 2007
Q3 2007
Q4 2007
Q1 2008
Q2 2008
Q3 2008
Q4 2008
8.63979
8.76305
8.84754
9.07422
13.90274
13.80349
14.06345
13.70845
12.98442
12.43080
11.79629
11.45905
11.27362
11.08907
10.86638
10.75825
10.35959
10.21916
10.05799
9.90427
9.87592
9.63247
9.57800
9.60698
9.46682
9.36959
9.39660
9.35636
9.27257
9.27212
9.15840
9.27765
9.09835
9.05966
8.91037
8.97301
8.82309
8.76944
8.72466
8.75618
8.71325
8.68669
8.68254
8.90419
8.74435
8.79992
8.80892
8.55702
8.44807
8.30683
7.93921
8.01787
7.80382
7.70193
7.65738
7.69864
7.50131
7.39146
7.39876
7.24059
6.98014
6.54328
6.28323
6.32115
0.03126
0.02983
0.02719
0.02694
0.02739
0.02646
0.02452
0.02396
0.02419
0.02342
0.02230
0.02197
0.02196
0.02135
0.02015
0.01955
0.02032
0.01939
0.01842
0.01885
0.01912
0.01829
0.01819
0.01829
0.01765
0.01729
0.01617
0.01493
0.01614
0.01553
0.01410
0.01386
0.01498
0.01452
0.01366
0.01346
0.01396
0.01332
0.01242
0.01158
0.01259
0.01332
0.01094
0.01016
0.01127
0.01074
0.00982
0.00930
0.01003
0.00953
0.00891
0.00819
0.00886
0.00866
0.00834
0.00735
0.00784
0.00739
0.00712
0.00647
0.00642
0.00639
0.00663
0.00701
PE
NFA
BT
TAR
PR
PE/CPI
Foreign assets/CPIUSA
(M+X)/GDP
Applied MFN
(GDP/Nº of Em.).
313.04552
304.25273
338.36260
358.28015
371.84898
469.14532
581.13045
682.71212
763.94292
817.03633
924.11489
1021.06642
1105.52620
1259.24735
1300.80952
1419.49054
1613.51339
1724.36413
1877.52911
1967.75613
1944.24381
1931.14334
1940.25580
1981.98877
1981.57158
1971.23133
2003.84120
2083.74465
2074.76753
2075.05804
2064.10447
2114.04267
2124.53999
2259.62176
2418.21437
2653.45005
2595.27330
2660.91287
2794.45566
3033.16845
3232.50866
3489.25394
3817.04423
3989.10279
4280.72974
4532.31179
4904.85692
5825.96056
6281.75635
6793.03541
7258.53275
7792.65798
8314.31495
8827.89992
9453.14712
10560.51927
11762.56199
12810.59372
14363.74606
15625.30299
17558.89729
19197.83162
20618.99447
22106.13467
2.1621
2.3892
2.3305
2.3151
2.0647
2.4191
2.3981
2.3990
2.2290
2.4878
2.5014
2.3737
2.1287
2.3054
2.3184
2.2128
2.0506
2.2184
2.2393
2.3005
1.8982
2.0368
1.9873
1.9631
1.6290
1.8300
1.9118
1.8220
1.8282
1.9673
2.0003
1.9242
1.7659
1.8083
1.8500
1.6738
1.5640
1.7217
1.8452
1.7019
1.6896
1.9564
1.7875
1.7332
1.7690
1.9113
1.8809
1.7806
1.7095
1.8855
1.9266
1.7638
1.7493
1.8489
1.9901
1.7841
1.6734
1.7635
1.8425
1.7061
1.5110
1.6239
1.7718
1.7536
39.87482
40.17561
40.18674
39.60401
36.34620
36.62805
37.05760
36.82651
22.40000
22.89574
22.56828
22.14602
23.65498
23.45448
23.53208
23.60432
17.60723
17.50343
17.57971
17.43083
17.47821
17.22967
17.26622
17.39115
17.10891
16.90807
17.24820
17.32795
16.94782
16.95348
17.01006
16.90272
15.89049
15.72881
15.79250
15.64399
12.37561
12.30936
12.41557
12.34905
11.25259
11.16817
11.22828
11.30129
10.41936
10.50803
10.57323
10.48434
10.66558
10.68741
10.70917
10.66558
9.67584
9.78364
9.79216
9.67584
9.77966
9.81830
9.88541
9.87476
9.42562
9.57838
9.60240
9.33018
0.050238
0.042738
0.047284
0.048215
0.055901
0.055911
0.053259
0.044235
0.061457
0.064117
0.077780
0.072833
0.066743
0.074673
0.088405
0.078601
0.072030
0.074015
0.091546
0.086549
0.076774
0.089123
0.096928
0.081101
0.081757
0.120515
0.122719
0.158789
0.087789
0.079451
0.084293
0.089488
0.093855
0.099487
0.133734
0.138436
0.101392
0.091700
0.093279
0.089790
0.110519
0.118134
0.130805
0.131203
0.120421
0.118624
0.143529
0.159872
0.131883
0.122297
0.132711
0.142318
0.146140
0.104837
0.120788
0.150568
0.126226
0.121023
0.126558
0.143957
0.151179
0.159608
0.188459
0.363619
9.3575
9.6231
9.7298
9.5964
9.8871
10.4360
10.1651
10.3948
11.3646
12.0536
12.9482
13.2633
13.8268
14.4929
15.0975
15.1643
16.2436
17.3113
16.9085
17.0272
17.8814
17.4526
18.4882
18.0598
19.5486
29.6732
32.1071
56.8180
24.6755
35.6679
37.1037
64.9828
29.1005
42.6393
45.4567
75.9031
36.4718
49.2458
53.5310
87.4657
39.5040
58.7391
56.4998
92.3499
44.5731
37.7040
39.6481
41.7582
41.9457
37.4699
40.0385
39.9256
43.7729
37.4424
39.9400
47.6779
52.1811
46.1728
47.7575
49.3080
52.1312
49.0127
52.3829
55.9348
Source: IFS/IMF - BLS - WTO/TRAINS - Reuters
32
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International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Table 9:
Table 9: Brazil. Real exchange rate (RER) and its real determinants (1993 - 2008)
Unit root test (Augmented Dickey - Fuller)
MacKinnon (1996) one-sided p-values
No trend
Intercept
no intercept
Trend and
No trend
Intercept
no intercept
Intercept
Trend and
Intercept
Real exchange rate: Order of integration = 1
RER in levels
0.773
-1.323
-2.398 RER in 1st differences
-4.757
-5.076
-4.996
1% level
-2.603
-3.542
-4.113 1% level
-2.603
-3.542
-4.116
5% level
-1.946
-2.910
-3.484 5% level
-1.946
-2.910
-3.485
10% level
-1.613
-2.593
-3.170 10% level
-1.613
-2.593
-3.171
2
2
1
1
1
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0.573
1 SIC (maxlag=9)
No
0.799
0.791
Terms of trade: Order of integration = 1
TT in levels
1.969
-1.771
-2.426 TT in 1st differences
-12.062
-7.316
-7.290
1% level
-2.603
-3.540
-4.113 1% level
-2.603
-3.542
-4.116
5% level
-1.946
-2.909
-3.484 5% level
-1.946
-2.910
-3.485
10% level
-1.613
-2.592
-3.170 10% level
-1.613
-2.593
-3.171
SIC (maxlag=9)
2
1
0
1
1
Structural Break
No at 1 and 5%
Chow p-value F
0.091
No
1 SIC (maxlag=9)
No
0.408
0.162
Public Expenditure: Order of integration = 0
PE in levels
-4.197
-3.614
-9.405
1% level
-2.607
-3.546
-4.121
5% level
-1.947
-2.912
-3.488
10% level
-1.613
-2.594
-3.172
7
4
4
SIC (maxlag=9)
Net Foreign Assets: Order of integration = 1
NFA in levels
2.495
1.284
-5.439
-5.665
-5.881
1% level
-2.602
-3.538
-4.110 1% level
-2.603
-3.540
-4.113
5% level
-1.946
-2.908
-3.483 5% level
-1.946
-2.909
-3.484
10% level
-1.613
-2.592
-3.169 10% level
-1.613
-2.592
-3.170
0
0
0
0
0
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0.846
0.357 NFA in 1st differences
0 SIC (maxlag=9)
No
0.650
0.444
Balance of Trade: Order of integration = 1
BT in levels
0.930
-0.683
-8.530 BT in 1st differences
-11.155
-11.192
-11.164
1% level
-2.604
-3.544
-4.110 1% level
-2.604
-3.544
-4.118
5% level
-1.946
-2.911
-3.483 5% level
-1.946
-2.911
-3.487
10% level
-1.613
-2.593
-3.169 10% level
-1.613
-2.593
-3.172
3
3
2
2
2
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0 SIC (maxlag=9)
No
0.949
0.869
0.287
TAR in levels
-0.680
-3.510
-4.481 TAR in 1st differences
-10.430
-10.352
-10.270
1% level
-2.603
-3.538
-4.110 1% level
-2.603
-3.540
-4.113
5% level
-1.946
-2.908
-3.483 5% level
-1.946
-2.909
-3.484
10% level
-1.613
-2.592
-3.169 10% level
-1.613
-2.592
-3.170
1
0
Tariffs: Order of integration = 1
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0.675
0 SIC (maxlag=9)
No at 1 and 5%
0.432
0.054
Productivity: Order of integration = 1
PR in levels
1.104
-0.815
-2.285 PR in 1st differences
-3.407
-3.599
-4.973
1% level
-2.605
-3.546
-4.121 1% level
-2.605
-3.546
-4.131
5% level
-1.946
-2.912
-3.488 5% level
-1.946
-2.912
-3.492
10% level
-1.613
-2.594
-3.172 10% level
-1.613
-2.594
-3.175
4
4
3
3
6
SIC (maxlag=9)
Structural Break
Chow p-value F
No
4 SIC (maxlag=9)
No at 1 and 5% No at 1 and 5%
0.455
0.058
0.058
Source: Author's calculations
Note: RER, PE, NFA, BT, TAR and PR in levels. TT in logarithms
Breaking point: 1995q1. SIC models maxlag ? 3: 1995q4. PE and PR with trend and intercept: 1996q1.
33
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International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Table 10:
Table 10: Russia. Real exchange rate (RER) and its real determinants (1995 - 2008)
Unit root test (Augmented Dickey - Fuller)
MacKinnon (1996) one-sided p-values
No trend
Intercept
no intercept
Trend and
No trend
Intercept
no intercept
Intercept
Trend and
Intercept
Real exchange rate: Order of integration = 2
RER in levels
1.902
0.032
-2.637 RER in 1st differences
-0.846
-2.916
-2.709
1% level
-2.609
-3.560
-4.145 1% level
-2.610
-3.560
-4.141
5% level
-1.947
-2.918
-3.499 5% level
-1.947
-2.918
-3.497
10% level
-1.613
-2.597
-3.179 10% level
-1.613
-2.597
-3.178
2
2
2
1
SIC (maxlag=9)
Structural Break
No
Chow p-value F
3 SIC (maxlag=9)
No
No
Structural Break
0.915
0.774
RER in 2nd differences
-5.333
-5.322
-5.291
1% level
-2.610
-3.563
-4.145
5% level
-1.947
-2.919
-3.499
10% level
-1.613
-2.597
-3.179
1
1
1
SIC (maxlag=9)
0.216 Chow p-value F
No
No
1
No
0.8364
0.8704
0.1658
Terms of trade: Order of integration = 1
TT in levels
0.693
-1.303
-2.610 TT in 1st differences
-7.554
-7.593
-7.814
1% level
-2.608
-3.555
-4.134 1% level
-2.609
-3.560
-4.141
5% level
-1.947
-2.916
-3.494 5% level
-1.947
-2.918
-3.497
10% level
-1.613
-2.596
-3.176 10% level
-1.613
-2.597
-3.178
0
0
1
1
1
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No at 1%
0.300
0 SIC (maxlag=9)
No at 1 and 5%
0.028
0.078
Public Expenditure: Order of integration = 1
PE in levels
2.120
1.063
-1.328 PE in 1st differences
-6.724
-7.099
-7.783
1% level
-2.608
-3.555
-4.134 1% level
-2.608
-3.557
-4.137
5% level
-1.947
-2.916
-3.494 5% level
-1.947
-2.917
-3.495
10% level
-1.613
-2.596
-3.176 10% level
-1.613
-2.596
-3.177
0
0
0
0
0
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0.290
0 SIC (maxlag=9)
No
0.326
0.177
Net Foreign Assets: Order of integration = 0
NFA in levels
-5.794
-6.518
-6.497
1% level
-2.611
-3.565
-4.148
5% level
-1.947
-2.920
-3.500
10% level
-1.613
-2.598
-3.180
4
4
4
SIC (maxlag=9)
Balance of Trade: Order of integration = 1
BT in levels
1.793
0.865
-0.579 BT in 1st differences
-4.382
-4.653
-5.136
1% level
-2.610
-3.563
-4.145 1% level
-2.610
-3.563
-4.145
5% level
-1.947
-2.919
-3.499 5% level
-1.947
-2.919
-3.499
10% level
-1.613
-2.597
-3.179 10% level
-1.613
-2.597
-3.179
3
3
2
2
2
SIC (maxlag=9)
Structural Break
No at 1 and 5% No at 1 and 5%
Chow p-value F
3 SIC (maxlag=9)
Si
0.065
0.052
0.001
TAR in levels
-0.559
-3.841
-4.917 TAR in 1st differences
-11.328
-11.225
-11.191
1% level
-2.608
-3.555
-4.134 1% level
-2.608
-3.557
-4.137
5% level
-1.947
-2.916
-3.494 5% level
-1.947
-2.917
-3.495
10% level
-1.613
-2.596
-3.176 10% level
-1.613
-2.596
-3.177
1
0
0
0
0
Tariffs: Order of integration = 1
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No at 1%
0.972
0 SIC (maxlag=9)
No
0.040
0.828
Productivity: Order of integration = 2
PR in levels
3.907
1.930
-2.324 PR in 1st differences
-1.658
-2.940
-4.660
1% level
-2.613
-3.571
-4.157 1% level
-2.611
-3.565
-4.157
5% level
-1.948
-2.922
-3.504 5% level
-1.947
-2.920
-3.504
10% level
-1.613
-2.599
-3.182 10% level
-1.613
-2.598
-3.182
6
6
3
3
SIC (maxlag=9)
Structural Break
Chow p-value F
No
No
6 SIC (maxlag=9)
No
Structural Break
0.915
0.926
PR in 2nd differences
-5.039
-5.000
-5.065
1% level
-2.615
-3.578
-4.166
5% level
-1.948
-2.925
-3.509
10% level
-1.612
-2.601
-3.184
6
6
6
SIC (maxlag=9)
0.933 Chow p-value F
No
0.6621
No
0.7463
5
No
0.9318
Source: Author's calculations
Note: RER, PE, NFA, BT, TAR y PR in levels. TT in logarithms
Breaking point: 2005q2.
34
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International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Table 11:
Table 11: India. Real exchange rate (RER) and its real determinants (1993 - 2008)
Unit root test (Augmented Dickey - Fuller)
MacKinnon (1996) one-sided p-values
No trend
Intercept
no intercept
Trend and
No trend
Intercept
no intercept
Intercept
Trend and
Intercept
Real exchange rate: Order of integration = 1
RER in levels
1.588
-1.674
-2.004 RER in 1st differences
-3.110
-3.560
-3.345
1% level
-2.603
-3.550
-4.113 1% level
-2.603
-3.540
-4.113
5% level
-1.946
-2.914
-3.484 5% level
-1.946
-2.909
-3.484
10% level
-1.613
-2.595
-3.170 10% level
-1.613
-2.592
-3.170
1
6
0
0
0
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0.888
1 SIC (maxlag=9)
No
0.841
0.885
Terms of trade: Order of integration = 1
TT in levels
0.231
-1.829
-1.314
-1.183
-6.279
1% level
-2.608
-3.557
-4.131 1% level
-2.608
-3.557
-4.131
5% level
-1.947
-2.917
-3.492 5% level
-1.947
-2.917
-3.492
10% level
-1.613
-2.596
-3.175 10% level
-1.613
-2.596
-3.175
9
9
8
8
6
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0.738
0.733 TT in 1st differences
7 SIC (maxlag=9)
No
0.539
0.516
Public Expenditure: Order of integration = 1
PE in levels
4.683
2.634
-3.403
-11.551
-12.332
1% level
-2.604
-3.544
-4.118 1% level
-2.605
-3.544
-4.118
5% level
-1.946
-2.911
-3.487 5% level
-1.946
-2.911
-3.487
10% level
-1.613
-2.593
-3.172 10% level
-1.613
-2.593
-3.172
3
3
3
2
2
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0.930
0.614 PE in 1st differences
3 SIC (maxlag=9)
No
0.921
0.871
Net Foreign Assets: Order of integration = 1
NFA in levels
0.282
-0.720
-2.323 NFA in 1st differences
-3.122
-3.324
-3.235
1% level
-2.603
-3.540
-4.113 1% level
-2.603
-3.540
-4.113
5% level
-1.946
-2.909
-3.484 5% level
-1.946
-2.909
-3.484
10% level
-1.613
-2.592
-3.170 10% level
-1.613
-2.592
-3.170
1
1
0
0
0
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0.886
1 SIC (maxlag=9)
No
0.928
0.692
Balance of Trade: Order of integration = 1
BT in levels
4.844
2.596
0.183
-12.196
-13.091
1% level
-2.603
-3.542
-4.116 1% level
-2.608
-3.542
-4.116
5% level
-1.946
-2.910
-3.485 5% level
-1.947
-2.910
-3.485
10% level
-1.613
-2.593
-3.171 10% level
-1.613
-2.593
-3.171
2
2
7
1
1
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0.175 BT in 1st differences
2 SIC (maxlag=9)
No
0.402
0.567
0.612
TAR in levels
-1.909
-0.903
-2.742 TAR in 1st differences
-9.161
-9.642
-9.560
1% level
-2.602
-3.538
-4.110 1% level
-2.603
-3.540
-4.113
5% level
-1.946
-2.908
-3.483 5% level
-1.946
-2.909
-3.484
10% level
-1.613
-2.592
-3.169 10% level
-1.613
-2.592
-3.170
0
0
0
0
0
Tariffs: Order of integration = 1
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0.219
0 SIC (maxlag=9)
No
0.201
0.278
Productivity: Order of integration = 1
PR in levels
2.110
-1.731
-1.720 PR in 1st differences
-3.545
-4.932
-5.277
1% level
-2.606
-3.550
-4.121 1% level
-2.605
-3.550
-4.127
5% level
-1.947
-2.914
-3.488 5% level
-1.946
-2.914
-3.491
10% level
-1.613
-2.595
-3.172 10% level
-1.613
-2.595
-3.174
SIC (maxlag=9)
6
6
3
5
5
Structural Break
No at 1 and 5%
Chow p-value F
0.052
No at 1%
4 SIC (maxlag=9)
No
0.014
0.150
Source: Author's calculations
Note: RER, PE, NFA, BT, TAR y PR in levels. TT in logarithms
Breaking point: 1995q1. SIC models maxlag ? 3: 1996q4. TT: 1998q3.
35
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April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Table 12:
Table 12: China. Real exchange rate (RER) and its real determinants (1993 - 2008)
Unit root test (Augmented Dickey - Fuller)
MacKinnon (1996) one-sided p-values
No trend
Intercept
no intercept
Trend and
No trend
Intercept
no intercept
Intercept
Trend and
Intercept
Real exchange rate: Order of integration = 1
RER in levels
-0.641
-1.423
-3.636 RER in 1st differences
-3.943
-7.106
-7.212
1% level
-2.603
-3.542
-4.110 1% level
-2.604
-3.540
-4.113
5% level
-1.946
-2.910
-3.483 5% level
-1.946
-2.909
-3.484
10% level
-1.613
-2.593
-3.169 10% level
-1.613
-2.592
-3.170
2
2
2
0
0
SIC (maxlag=9)
Structural Break
No
Chow p-value F
0 SIC (maxlag=9)
No
0.993
No
0.118
0.476
Terms of trade: Order of integration = 1
TT in levels
7.315
0.304
-1.390 TT in 1st differences
-1.899
-9.711
-9.638
1% level
-2.604
-3.544
-4.118 1% level
-2.605
-3.544
-4.118
5% level
-1.946
-2.911
-3.487 5% level
-1.946
-2.911
-3.487
10% level
-1.613
-2.593
-3.172 10% level
-1.613
-2.593
-3.172
3
3
3
2
2
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0.444
3 SIC (maxlag=9)
No at 1%
0.678
0.040
Public Expenditure: Order of integration = 1
PE in levels
0.642
-1.155
-1.987 PE in 1st differences
-4.377
-4.600
-4.580
1% level
-2.605
-3.546
-4.124 1% level
-2.605
-3.546
-4.121
5% level
-1.946
-2.912
-3.489 5% level
-1.946
-2.912
-3.488
10% level
-1.613
-2.594
-3.173 10% level
-1.613
-2.594
-3.172
4
4
3
3
3
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0.436
5 SIC (maxlag=9)
No at 1 and 5%
0.144
0.088
Net Foreign Assets: Order of integration = 0
NFA in levels
9.194
8.948
7.150
1% level
-2.603
-3.540
-4.113
5% level
-1.946
-2.909
-3.484
10% level
-1.613
-2.592
-3.170
1
1
1
SIC (maxlag=9)
Balance of Trade: Order of integration = 1
BT in levels
-2.178
-1.639
-2.125 BT in 1st differences
-3.347
-4.693
-4.823
1% level
-2.607
-3.553
-4.121 1% level
-2.605
-3.553
-4.131
5% level
-1.947
-2.915
-3.488 5% level
-1.946
-2.915
-3.492
10% level
-1.613
-2.595
-3.172 10% level
-1.613
-2.595
-3.175
7
7
3
6
6
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
4 SIC (maxlag=9)
No at 1%
0.848
0.652
0.030
TAR in levels
-1.751
-1.296
-1.471 TAR in 1st differences
-6.931
-6.301
-4.842
1% level
-2.608
-3.555
-4.134 1% level
-2.608
-3.555
-4.134
5% level
-1.947
-2.916
-3.494 5% level
-1.947
-2.916
-3.494
10% level
-1.613
-2.596
-3.176 10% level
-1.613
-2.596
-3.176
8
8
7
7
7
Tariffs: Order of integration = 1
SIC (maxlag=9)
Structural Break
No
Chow p-value F
No
0.480
8 SIC (maxlag=9)
No
0.132
0.123
Productivity: Order of integration = 1
PR in levels
1.645
0.972
-0.869 PR in 1st differences
-4.101
-4.260
-4.379
1% level
-2.602
-3.538
-4.110 1% level
-2.603
-3.540
-4.113
5% level
-1.946
-2.908
-3.483 5% level
-1.946
-2.909
-3.484
10% level
-1.613
-2.592
-3.169 10% level
-1.613
-2.592
-3.170
0
0
0
0
0
SIC (maxlag=9)
Structural Break
Chow p-value F
No
0 SIC (maxlag=9)
No at 1 and 5% No at 1 and 5%
0.220
0.056
0.074
Source: Author's calculations
Note: RER, PE, NFA, BT, TAR y PR in levels. TT in logarithms
Breaking point: 2001q1.
36
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International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Table 17:
Brazil. Estimated Cointegrated Vectors in Johansen Estimation
Cointegration with unrestricted intercepts and restricted trends in the VAR
*******************************************************************************
63 observations from 1993Q2 to 2008Q4. Order of VAR = 1, chosen r =2.
List of variables included in the cointegrating vector:
RER
TT
NFA
BT
TAR
PR
Trend
*******************************************************************************
RER
Vector 1
Vector 2
.015007
.10323
( -1.0000) ( -1.0000)
TT
.24669
-.25241
( -16.4388) ( 2.4450)
NFA
.1309E-3
-.1886E-3
(-.0087201) ( .0018271)
BC
-.82819
-.23788
( 55.1881) ( 2.3043)
TAR
.0089794
-.027366
( -.59836) ( .26509)
PR
43.4808
926.4040
( -2897.4) ( -8973.9)
Trend
.0072040
-.014051
( -.48006) ( .13611)
*******************************************************************************
37
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International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Table 18:
Russia. Estimated Cointegrated Vectors in Johansen Estimation
Cointegration with unrestricted intercepts and restricted trends in the VAR
*******************************************************************************
54 observations from 1995Q3 to 2008Q4. Order of VAR = 1, chosen r =3.
List of variables included in the cointegrating vector:
RER
TT
PE
BT
TAR PR
Trend
*******************************************************************************
Vector 1
RER
-.039532
Vector 2
Vector 3
.0053003
.11475
( -1.0000) ( -1.0000) ( -1.0000)
TT
.067535
.87211
-.12421
( 1.7084) (-164.5407) ( 1.0825)
PE
.0060780
.22851
.068509
( .15375) ( -43.1131)
BT
-.8570E-4
-.6825E-3
( -.59704)
-.5189E-3
(-.0021679) ( .12877) ( .0045220)
TAR
.0021087
.017660
-.0082495
( .053341) ( -3.3318)
PR
-1115.0
437.5683
( .071892)
-90.9423
( -28205.4) ( -82555.6) ( 792.5366)
Trend
.0023083
-.0022578
-.0069608
( .058391) ( .42597) ( .060662)
*******************************************************************************
38
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International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Table 19:
India. Estimated Cointegrated Vectors in Johansen Estimation
Cointegration with unrestricted intercepts and restricted trends in the VAR
*******************************************************************************
63 observations from 1993Q2 to 2008Q4. Order of VAR = 1, chosen r =4.
List of variables included in the cointegrating vector:
RER
TT
PE
NFA
BT
TAR
PR
Trend
*******************************************************************************
Vector 1
RER
Vector 2
-.0042334
( -1.0000)
TT
PE
.31603
-1.1666
( -48.2293)
( 55.5271)
.042004
.022631
( -3.4537)
.3842E-3
( .090759)
BT
( -11.8040)
TAR
-167.2600
Trend
.0065519
( 1.5477)
( 74.2894)
-.020329
( 12.0326)
217.0217
(-128453.9)
-.036149
( 1.7205)
-.011781
( 6.9729)
65.0128
( -3094.3)
.4037E-3
( -.23897)
.0072467
( -.34491)
-.031647
( 4.8296)
-.12551
.2455E-3
( -1.2150)
698.1220
(-106538.9)
(-238.6794)
.025528
-.012444
( 1.8990)
.40325
( -.011685)
.017509
-.0043343
( -39510.0)
.6172E-4
( -2.6720)
( -1.0238)
PR
( 3.9690)
.0016895
( -1.0000)
-.083390
(-.0094188)
-.049971
Vector 4
.021010
( -1.0000)
( 9.9221)
NFA
.0065527
( -1.0000)
-.36517
( -86.2591)
Vector 3
.0053966
( -3.1942)
*******************************************************************************
39
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International Journal of Academic Research in Economics and Management Sciences
April 2012, Vol. 1, No. 2
ISSN: 2226-6348
Table 20:
China. Estimated Cointegrated Vectors in Johansen Estimation
Cointegration with unrestricted intercepts and restricted trends in the VAR
*******************************************************************************
63 observations from 1993Q2 to 2008Q4. Order of VAR = 1, chosen r =2.
List of variables included in the cointegrating vector:
RER
TT
PE
BT
TAR PR
Trend
*******************************************************************************
RER
Vector 1
Vector 2
-.014790
-.041091
( -1.0000) ( -1.0000)
TT
-1.6786
1.2610
(-113.4953) ( 30.6882)
PE
.0057178
.0039894
( .38659) ( .097087)
BT
.081376
.97740
( 5.5019) ( 23.7864)
TAR
-.0080277
-.0021260
( -.54276) ( -.051740)
PR
1.8359
.0066545
( 124.1248) ( .16195)
Trend
-.050473
.034552
( -3.4126) ( .84086)
*******************************************************************************
40
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