Heads I Win, Tails You Lose: Asymmetry in
Aggregate Exchange Rate Pass-Through∗
Raphael Brun-Aguerre†
J.P. Morgan, London
Ana-Maria Fuertes‡
Cass Business School, City University London
Matthew Greenwood-Nimmo§
Faculty of Business & Economics, University of Melbourne
December 10, 2014
Abstract
This paper studies the response of import prices to exchange rate shocks using a flexible
modelling framework that allows for empirical tests of short- and long-run asymmetric passthrough. A quarterly sample from 1980 to 2010 for 33 countries reveals stronger pass-through
of depreciations than appreciations over long horizons. The asymmetry in pass-through is
robust to different country groupings and appears positively linked to import dependence
but the link weakens with freedom to trade. Our findings suggest that, since the passthrough asymmetry uncovered is welfare-reducing for consumers, heavily import-dependent
economies can benefit from trade liberalization.
Keywords: Exchange Rate Pass-Through; Asymmetry; Nonlinear ARDL Model; Random Coefficients Panel Data Model; Emerging Markets.
JEL Classifications: F10; F14; F30; F31.
∗
Correspondence to: M.J. Greenwood-Nimmo, 3.12 Faculty of Business and Economics, University of Melboune,
Carlton 3053, Australia. We would like to thank Charlie Cai, Annina Kaltenbrunner, Minjoo Kim, Donald
MacLaren, Phil McCalman, Viet Nguyen, Adrian Pagan, Miles Parker, Kalvinder Shields, Yongcheol Shin, Ron
Smith, Benjamin Wong and participants at the 7th International Conference on Computational and Financial
Econometrics, CFE 2014 (December 2013), the New Zealand Macroeconomic Dynamics Workshop (April 2014),
the Econometric Society Australasian Meeting (July 2014), and the 12th INFINITI Conference on International
Finance (June 2014) for helpful comments. The views expressed herein do not reflect those of J.P. Morgan.
†
Email: [email protected]
‡
Email: [email protected]
§
Email: [email protected]. Tel: +61 3 8344 5354.
1
1
Introduction
A deep understanding of the exchange rate pass-through (ERPT) phenomenon is important to
make accurate predictions on the effectiveness of monetary policies and to inform important
debates on the international transmission of shocks, exchange rate regime optimality and global
trade imbalances. ERPT is complete when exporters decide not to ‘shoulder’ exchange rate
changes but instead prefer to maintain their export prices fixed as a markup over marginal costs
– a strategy known as producer currency pricing (PCP). On the other hand, the prices of the
final goods that importers buy are insensitive to exchange rate shocks when exporters let their
margins fluctuate in order to absorb the shocks – a strategy known as local currency pricing
(LCP). These two theoretical scenarios imply unit and zero ERPT elasticities, respectively.
The law of one price (LOOP) predicts complete and symmetric ERPT under the assumption
that markets are perfectly competitive and frictionless. This implies that all exchange rate
shocks, irrespective of whether they represent appreciation or depreciation of a given currency,
ought to be rapidly and fully reflected into import prices.1 In practice many firms operate in
imperfectly competitive markets subject to various frictions which, in turn, implies that the
actual exchange rate pass-through (ERPT) may be sluggish, incomplete and asymmetric.2
The present paper complements the literature by providing evidence on the extent and, more
pertinently, the asymmetric nature of exchange rate pass-through into import prices at aggregate country level over different horizons.3 Our work can be differentiated from extant papers in
several ways. First, we implement the nonlinear autoregressive distributed lag (NARDL) framework proposed by Shin et al. (2014) which offers great flexibility to investigate the presence of
asymmetries both in the short- and long-run. Our analysis represents the first attempt to extend
the original time-series NARDL approach to a panel setting in order to produce more reliable
inferences by additionally exploiting the cross-section variation for various country groupings.
Secondly, we investigate links between the degree of asymmetric ERPT that an importing
1
Throughout this paper, the term appreciation (depreciation) will refer to an increase (decrease) in the value
of the importer’s local currency relative to the exporter’s currency.
2
Kreinin (1977) shows that the 1970’s currency realignments were only partly passed into import prices in
the US market (by 50 percent), Germany (60 percent) and Japan (70 percent. The 1990s currency crisis did
not materialize into high inflation which represents further evidence of incomplete ERPT. For instance, although
the Finnish banking crisis led to a cumulative depreciation of 29% between 1991 and the beginning of 1993, the
average CPI inflation over the same period was just 3.5%. Similarly, despite a 50% depreciation of the Korean
Won between 1996 and the beginning of 1998, the average inflation was just 5%. A more recent but yet unsettled
debate is whether the ERPT is endogenously driven by micro and/or macro factors of the importing economy
(Brun-Aguerre et al., 2012; Bussière et al., 2013; Choudhri and Hakura, 2006; Campa and Goldberg, 2005).
3
By studying the influence of exchange rate shocks on import prices at the ‘dock’ we focus on the narrowest
notion of pass-through. Thus we sidestep the need to control for confounding factors (ultimately reflected into
consumer prices) such as tariffs, local transportation costs, distribution costs and retail costs.
2
economy faces and various micro and macro factors that could determine it. Last but not least,
our analysis is based on a wide cross-section of 33 countries: 14 emerging markets (EMs) and
19 developed markets (DMs). Despite the growing importance of EMs in international trade,
very few papers have studied such a comprehensive set of both EMs and DMs.4 Furthermore,
in order to obtain more accurate ERPT measures we construct a trade-weighted foreign export
price for each importing economy instead of relying on proxies.5
One key empirical finding from our analysis is that depreciations are passed into import
prices more vigorously than appreciations over the long-run with no significant differences in
this regard between EMs and DMs. Thus the fear of floating often shown by EMs may be
unfounded as the currency mismatch argument is not supported by our analysis. We also
confirm a positive link between the long-run ERPT asymmetry and import dependence which
suggests that the pricing decisions of exporting firms are influenced by market power. However,
the aforementioned link appears weaker for importing economies that enjoy greater freedom of
trade. This moderating effect of freedom-to-trade on the degree of pass-through asymmetry is
well aligned with the conventional wisdom that greater openness enhances market competition.
Furthermore, the long-run asymmetry is positively linked to the output gap which may be a
reflection of exporters’ strategic search for revenue from rapidly-growing economies.
Our findings speak both to policymakers and to a broad academic literature on pass-through.
Stronger pass-through for depreciations than appreciations over the long run suggests protracted
downward import-price rigidity and that, on the whole, exporters are able to benefit from market
power in relatively weak competition environments for many classes of traded goods. In countries
where imports account for a large share of the representative consumption basket, this downward
import-price stickiness will be reflected in both realised and expected inflation. This may offer
a partial explanation of the fact that inflation did not decline substantially in most countries
during and after the global financial crisis despite significant contractions in aggregate demand.6
Asymmetric ERPT impairs the ability of exchange rate changes to correct trade imbalances,
and complicates the conduct of monetary policy. One important economic implication from
our analysis is that by encouraging trade freedom, it is possible to subject exporters to greater
market discipline, reducing the scope for rent-seeking pass-through behaviour.
Our paper is related to several strands of the literature. On the one hand, it speaks to stud4
Webber (2000) considers both DMs and EMs but the analysis is confined to 8 Asian countries.
For example, Bussière (2007) uses producer price indices and Marazzi et al. (2005) use consumer price indices.
6
This issue was partially explained by a flattening of the Phillips curve in some countries (IMF, 2013). Asymmetric import pass-through is consistent with downward price rigidities through the trade channel.
5
3
ies that have theorized on the incentives of exporters to adopt different pass-through strategies
during phases of appreciation and depreciation. The capacity constraints theory states that
exporting firms operating at full capacity cannot accommodate the surge in demand resulting
from importing currency appreciation. Thus, exporters may rationally choose to retain appreciations by widening their markups, a strategy consistent with short-run downward import price
stickiness and the notion that prices rise faster than they fall (Peltzman, 2000). The market
share theory posits that foreign firms seeking to gain or defend market share may be willing to
retain depreciations by lowering their revenues in order to quote competitive prices, and to pass
appreciations which will reduce the goods’ prices for importers (Krugman, 1987; Marston, 1990).
However, this pricing strategy is unlikely to be pursued in the long-run. Moreover, the degree
of competition is likely to play a role as exporters operating in weakly competitive markets may
elect to systematically pass through depreciations so as to preserve markups (Bussière, 2007).
Finally, the technology switching theory advanced by Ware and Winter (1988) suggests that
exporters can afford to pass appreciations more strongly than depreciations if they can strategically alter the source of their production inputs (e.g., by switching from foreign to domestic
sources and vice versa) and the type of production technology employed. However, since technology switching (even at no cost) takes time to be implemented and contracts with input providers
are likely to have fixed terms, this mechanism cannot be reconciled with short-run asymmetries.
Our paper is most closely related to a sparse empirical literature that investigates asymmetric import pass-through behaviour. Most extant empirical models of pass-through only
accommodate short-run asymmetry and are estimated over a small cross-section of countries.
For example, Herzberg et al. (2003) analyzes asymmetric pass-through for the UK, Marazzi et
al. (2005) and Pollard and Coughlin (2004) focus on the US, Khundrakpam (2007) works with
Indian data and Bussière (2007) studies the G7 economies. The findings of these studies are
rather mixed. For example, Pollard and Coughlin (2004) document short-run sign asymmetry
for about half of 30 US industries with data from 1978 to 2000 but the direction of the effect
is ambiguous. By contrast, using aggregate UK data from 1975 to 2001, Herzberg et al. (2003)
cannot refute the hypothesis that the short-run import ERPT mechanism is linear. On the other
hand, based on his investigation of short-run ERPT to both import and export prices, Bussière
(2007) stresses that asymmetric pass-through behaviour cannot be ignored.
Long-run asymmetry in pass-through has been investigated in two studies. Working with
data for 8 Asian economies and using a model that can only accommodate long-run asymmetry,
Webber (2000) demonstrates that depreciations are passed more powerfully than appreciations
4
into import prices over long horizons. In their analysis of 4 developed economies – Germany,
Japan, the UK and the US – Delatte and Lopez-Villavicencio (2012) find evidence that depreciations feed into the general price level more strongly than appreciations over the long run.
The remainder of the paper is organized as follows. Section 2 discusses the data and Section 3
explains the methodology. The empirical results are presented in section 4. Section 5 concludes.
Further details of the dataset and methodology are provided as Appendix material.
2
Data Description
Our analysis focuses on a large panel of N = 33 importing economies, 14 of which are classified as
EMs and 19 as DMs. The countries are: Argentina† , Australia, Belgium/Luxembourg, Brazil† ,
Canada, Chile† , China† , Colombia† , Czech Republic† , Denmark, Finland, France, Germany,
Greece, Hong Kong† , Hungary† , Ireland, Israel† , Italy, Japan, Korea† , Mexico† , Netherlands,
Norway, New Zealand, Singapore† , South Africa† , Spain, Sweden, Switzerland, Thailand† , the
UK, and the US, where a
†
symbol denotes an EM. Collectively, these countries accounted for
69% of world imports in 2010, with 48% attributable to the DMs and 21% to the EMs.
Our analysis proceeds in three steps. First, we estimate the exchange rate elasticity of import
prices (hereafter, the ERPT elasticity) on a time-series basis. In order to test for asymmetric
pass-through, we employ a flexible NARDL model that allows the import price to respond
asymmetrically to depreciations and appreciations both in the long-run equilibrium path and
in the short-run dynamics. The NARDL models are estimated using import price, export price
and exchange rate time-series data for countries i = 1, 2, . . . , N .
The import price for country i, denoted pi,t , is an index that represents the domestic price
of goods and services at the dock. Third, the export price, denoted p∗i,t , is an effective index
that measures the foreign price of goods and services traded into country i from the rest of
the world. We compute this index as a relative trade-weighted average of export price indices,
PJ(i) j j∗
p∗i,t = j=1 wi,t
pt where j = 1, ..., J(i) denote the trading partners of importing country i, and
j
wi,t
is the import share or ratio of the imports in US$ received by country i from country j to
its total imports;7 accordingly, p∗i,t measures the ‘rest-of-the-world’ foreign export price faced
by country i. The exchange rate is defined as the local (importer’s) currency price of a unit
of the foreign (exporter’s) currency, and proxied by si,t = 1/N EERi , where N EERi is the
nominal effective exchange rate index of foreign currency per unit of domestic currency. The
7
Import trade data are obtained from the IMF’s Direction of Trade Statistics.
5
three variables are used in logarithms throughout our analysis.
The distribution of log quarterly changes (∆si,t , ∆pi,t , ∆p∗i,t )0 , summarised in Table 1, shows
considerably more volatility for EMs than DMs. The largest standard deviations of ∆si,t are
30.48% (Argentina) among EMs versus a much lower 4.54% (Japan) among DMs. The largest
quarterly depreciation (∆si,t > 0) across all countries stands at 71.54% for Brazil which is also
the country that displays the largest import price change on average over the sample period.
[Insert Table 1 around here]
The columns labelled ‘depr(+)’ and ‘appr(-)’ in Table 1 provide a count of the quarters when
the domestic currency appreciated and depreciated, respectively. The two counts are broadly
similar; for DMs, the number of quarterly depreciation ranges across countries from 34% to 65%,
and the corresponding range for EMs is 31% to 72%. For each of the variables in log levels, the
table reports the ADF test for the null hypothesis of unit root behaviour against the alternative
of stationarity, and the KPSS test for the same two hypotheses formulated in reverse order.
In conjunction with (unreported) results of the two tests applied to log quarterly changes, the
reported test statistics confirm that si,t , pi,t and p∗i,t are difference stationary.
At the second stage of our analysis we panel estimation and inference methods.8 By exploiting both the time variation and the cross-section variation in the data, it is possible to mitigate
noise and obtain more reliable pass-through estimates and hypotheses tests. We employ the
Mean Group panel estimation approach proposed by Pesaran and Smith (1995). To obtain a set
of baseline results, we begin by studying the full panel of N = 33 countries. Subsequently, we
group the countries into smaller subpanels; for instance, by testing the hypothesis of equality of
the DM and EM panel pass-through estimates we can investigate whether the extent of ERPT
depends on the level of economic and financial development of the importing economy. We
consider several other country groupings, as explained further below.
At the final stage, we conduct cross-section regressions to identify the economic factors that
drive the pass-through asymmetry. The candidates are drawn from the extant literature that
has sought to explain differences in the extent of pass-through across countries, and include the
economic development of the import market (DM versus EM), and its import dependence, FX
volatility, output gap, GDP per capita, inflation level and volatility (see e.g., Brun-Aguerre et
al., 2012; Choudhri and Hakura, 2006; Campa and Goldberg, 2005; Taylor, 2000).
Moving beyond the above economic factors, we consider two other potential drivers of asymmetry. Firstly, since demand for commodities is highly inelastic in the short-run due to habit
8
The panel is unbalanced and the longest data span is 1980Q1 to 2010Q4. See Appendix A for details.
6
formation, sunk costs and the costs associated with technology-switching, rent-seeking behaviour
by exporting firms may materialize in asymmetric ERPT for net commodity importers (i.e.,
depreciations are more strongly passed than appreciations). We therefore develop a ‘net commodity importing’ indicator by regressing quarterly exchange rate changes, ∆sit , on a constant
and quarterly changes in a broad commodity index, ∆Cit . Three regressions are run per country, respectively, using the (i) Goldman Sachs Commodity Index (GSCI), (ii) Dow Jones-UBS
Commodity Index (DJ-UBSCI), and (iii) Thomson Reuters/Jefferies CRB Index (TR/J CRB).
The indicator is constructed by averaging the three slope coefficients; negative (positive) values
signify a commodity currency (net commodity importer) country. Secondly, we evaluate the
role of trade freedom, which captures the extent of frictions to international trade introduced
by tariff structures, trade quotas, inefficient and/or corrupt administration and capital controls.
We use Item 4 of the Economic Freedom of the World index (EFW) compiled by Gwartney et al.
(2012) which is bounded between 0 and 10; higher EFW values signify greater freedom of trade.
Trade liberalization materializes into stronger market competition which imposes discipline on
exporters’ pricing policies, reducing the scope for opportunistic pass-through behaviour.
3
3.1
Methodology
Country-by-Country NARDL Modelling
The time dimension (T ) of the sample is large enough to accommodate full country heterogeneity
in our pass-through analysis by estimating a separate model per importing country. We adopt
the flexible NARDL modelling approach proposed by Shin et al. (2014). The NARDL modelling
framework involves the decomposition of the effective exchange rate into the partial sums
s+
i,t =
t
X
j=1
∆s+
i,j =
t
X
max (∆si,j , 0) , s−
i,t =
j=1
t
X
∆s−
i,j =
j=1
t
X
min (∆si,j , 0) ,
(1)
j=1
−
where si,t ≡ si,0 + s+
i,t + si,t . These partial sum processes separate out periods of depreciation of
−
the domestic currency (s+
i,t ) from periods of appreciation (si,t ). The initial value si,0 can be set
to zero without loss of generality. An asymmetric long-run equilibrium relation which nests the
−
conventional (symmetric) relation for si,t = s+
i,t = si,t is formalized as
− −
∗
pi,t = βi+ s+
i,t + βi si,t + γi pi,t + ui,t ,
7
(2)
where (βi+ , βi− , γi )0 are unknown long-run parameters. Equation (2) can be rewritten as ui,t =
pi,t − pei,t where pei,t is the equilibrium path of the import price for country i conditional on the
exchange rate and export price levels. The stationary zero-mean error process ui,t represents
deviations of country i ’s import price from its long-run equilibrium path. Substituting (2) into
the error-correction term, ρi (pi,t − pei,t ), of a standard linear ARDL(p, q, r) model yields the
following NARDL(p, q, r) model for the aggregate import price that the ith country faces
− −
∗
∆pi,t = αi + ρi pi,t−1 + θi+ s+
i,t−1 + θi si,t−1 + λi pi,t−1
+
p−1
X
j=1
ϕi,j ∆pi,t−j +
q−1 X
r−1
X
+
−
−
πi,j
∆s+
+
π
∆s
+
φi,j ∆p∗i,t−j + εi,t .
i,t−j
i,j
i,t−j
j=0
(3)
j=0
where εi,t ∼ i.i.d.(0, σi2 ). The main parameters of interest are the long-run ERPT elasticities
given by βi+ = −θi+ /ρi for depreciations and βi− = −θi− /ρi for appreciations, and the short-run
+
+
−
− 0 9
ERPT elasticities gathered in the vector (πi,0
, πi,1
, πi,0
, πi,1
). ,
10
The significance of the long-run equilibrium relation (2) can be assessed with the FP SS test
statistic proposed by Pesaran et al. (2001) or the tBDM test statistic of Banerjee et al. (1998).
The former is a non-standard F -test of the joint restriction H0 : ρi = βi+ = βi− = γi = 0 in (3),
while the latter is a non-standard t-test of the single restriction H0 : ρi = 0 against the alternative
HA : ρi < 0. For both tests, we employ the critical value bounds tabulated by Pesaran et al.
(2001) which are valid for variables with different (non)stationarity properties; this is pertinent
in our context given that partial sum decompositions may be observationally equivalent to
integrated processes of order 0 or 1, usually referred to as I(0) and I(1), respectively.
The NARDL model (3) is ideally suited to the empirical analysis of pass-through because it
is nonlinear-in-variables but linear-in-parameters, and readily estimable by OLS. Furthermore, it
accommodates asymmetry in the short- and long-run and nests a number of simpler pass-through
models. One case arises under the restriction βi+ = βi− = βi , which implies that the long-run
9
Equation (3) is equivalent to the error correction representation of the conditional model given in equation
(2.10) of Shin et al. (2014). By virtue of its conditional specification, the NARDL model provides valid estimation
and inference in the presence of weakly endogenous explanatory variables, xi,t , with the caveat that it is not
possible to identify contemporaneous causal effects between the elements of ∆pi,t and ∆xi,t without making
further assumptions. As most empirical pass-through studies in the literature, the NARDL framework adopted
in this paper builds on the assumption that import rate changes ∆pi,t do not contemporaneously affect exchange
rates, ∆si,t , and and export prices, ∆p∗i,t . This is a mild assumption in the present context since the sampling
frequency is quarterly; namely, although, a large rise in import prices may ultimately increase inflation to which
the Central Bank may react (hence, it may affect the exchange rate) the mechanism is unlikely to unfold within
one quarter due to delays in published inflation and Central Bank reaction functions which are more concerned
about year-on-year inflation. Nevertheless, no such assumption is required regarding the long-run parameters.
10
The long-run and short-run parameters characterizing the relation between import and export prices are
γi = −λi /ρi and (ϕi,1 , φi,0 , φi,1 )0 , respectively.
8
ERPT is symmetric. Cumulative short-run ERPT is symmetric if
Pq−1
j=0
+
πi,j
=
Pq−1
j=0
−
πi,j
, while
−
+
the impact ERPT is symmetric if πi,0
= πi,0
.
For each importing economy i = 1, ..., N in the sample, we evaluate three hypotheses concerning the magnitude of exchange rate pass-through into import prices (from 0 to complete)
and its nature (linear or asymmetric) in the the long run
Hypothesis 1 (Zero long-run ERPT) H01+ : βi+ = 0 for depreciations and H01− : βi− = 0 for
1+
1−
appreciations vs. alternatives HA
: βi+ > 0 and HA
: βi− > 0, respectively,
Hypothesis 2 (Complete long-run ERPT) H02+ : βi+ = 1 for depreciations and H02− : βi− =
2+
2−
1 for appreciations vs. alternatives HA
: βi+ < 1 and HA
: βi1− < 1, respectively,
3 : β + 6= β − ,
Hypothesis 3 (Symmetric long-run ERPT) H03 : βi+ = βi− vs. HA
i
i
another three counterpart hypotheses pertaining to impact pass-through behaviour
−
+
= 0 for
= 0 for depreciations and H04− : πi,0
Hypothesis 4 (Zero impact ERPT) H04+ : πi,0
−
+
4−
4+
: πi,0
> 0, respectively.
: πi,0
> 0 and HA
appreciations vs. alternatives HA
−
+
=1
= 1 for depreciations and H05− : πi,0
Hypothesis 5 (Complete impact ERPT) H05+ : πi,0
−
+
5−
5+
: πi,0
< 1, respectively.
: πi,0
< 1 and HA
for appreciations vs. alternatives HA
+
−
6 : π + 6= π − .
Hypothesis 6 (Symmetric impact ERPT) H06 : πi,0
= πi,0
vs. HA
i,0
i,0
and the short-run cumulative pass-through hypotheses
Pq−1 +
7−
Hypothesis 7 (Zero short-run ERPT) H07+ :
j=0 πi,j = 0 for depreciations and H0 :
Pq−1 −
7+ Pq−1 +
7− Pq−1 −
j=0 πi,j = 0 for appreciations vs. alternatives HA :
j=0 πi,j > 0 and HA :
j=0 πi,j > 0,
Pq−1 +
Hypothesis 8 (Complete short-run ERPT) H08+ :
j=1 πi,j = 1 for depreciations and
P
P
q−1 −
8+
+
8− Pq−1 −
H08− : j=0
πi,j = 1 for appreciations versus HA
: q−1
j=0 πi,j < 1 and HA :
j=0 πi,j < 1,
Hypothesis 9 (Symmetric short-run ERPT) H09 :
Pq−1 +
Pq−1 −
9:
tive HA
j=0 πi,j 6=
j=0 πi,j .
Pq−1
j=0
+
πi,j
=
Pq−1
j=0
−
πi,j
vs. the alterna-
The above hypotheses can be tested using the standard asymptotic Wald test and t test.
Additionally, in order to provide robust evidence, we also deploy the bootstrap test for asymmetry proposed by Shin et al. (2014) which is based on the cumulative dynamic multipliers
9
computed recursively from the parameters of the NARDL equation (3) reparameterized in lev−
els. The dynamic multipliers, denoted m+
i,h and mi,h trace the evolution of the import price
over periods h = 0, 1, 2, . . . , H in response to a unit depreciation and appreciation, respectively,
−
of the domestic currency in period h = 0. The linear combination m+
i,h − mi,h is a measure
−
of asymmetry in pass-through at horizon h. Bootstrap confidence intervals around m+
i,h − mi,h
for horizon h = 0, 1, ..., H quarters can be used to test whether the asymmetry at horizon h is
statistically significant; see Appendix B for details. Bootstrap inference has two main advantages relative to asymptotic inference. Firstly, it is valid in small samples which is pertinent in
our context because, recall that our panel is unbalanced, the time span is relatively small for
some countries; see Appendix A for details. Secondly, the bootstrap testing approach based on
dynamic multipliers can be used to evaluate asymmetry at any desired horizon.
3.2
Panel NARDL Modelling
By exploiting both the time and cross-section dimensions of the sample, panel estimation of
pass-through coefficients should increase the signal-to-noise ratio relative to the country-bycountry estimation. We deploy the panel Mean Group (MG) estimator of Pesaran and Smith
(1995) which is defined as an equal-weighted average of the individual country coefficients.
MG estimation allows for a simple and flexible random-coefficients formulation of the NARDL
equation (3) that allows for full country-heterogeneity in the parameters.11
+
+
−
− 0
Let Θi = (βi+ , πi,0
, πi,1
, βi− , πi,0
, πi,1
) denote the vector of ERPT coefficients for country
i = 1, ..., Ng , with Ng ≤ N representing the number of countries in the group at hand. The
PNg
MG
MG
panel MG estimator is defined as Θ̄
≡ N1g i=1
Θ̂i with covariance matrix V (Θ̄ ) ≡
PNg
MG
MG
1
)(Θ̂j − Θ̄ )0 which allows assessing Hypotheses 1 to 9 at the group
i=1 (Θ̂i − Θ̄
Ng (Ng −1)
level using standard asymptotic tests.
12
We also compute group-level dynamic multipliers for
depreciations and appreciations at horizons h = 0, 1, ..., H and the significance of the differential
is evaluated with bootstrap confidence bands; see Appendix B for details.
The first group contains the full set of countries (Ng = N = 33). Alternative groups are
based on the economic variables outlined in Section 2; for each economic variable, we compute
11
We also employed the Swamy (1970) random coefficients estimator which gives less weight to the noisier
country pass-through coefficients. However, for some country groupings, the covariance matrix of the Swamy
estimator was not positive definite in which case we followed Swamy’s suggestion and replaced it with the MG
covariance matrix. The MG and Swamy estimation led to qualitatively similar inferences, so we only present the
former. We also deployed the Pesaran (2004) test statistic for the null hypothesis of no cross-section dependence
computed from the residuals of the individual NARDL models. The small value of 0.823 obtained suggests that
accounting for a factor structure or for spatial dependence in our panel setting is not warranted.
12
Effectively, the panel MG approach presumes that the long-run parameters of interest are E(−θ+ /ρ) and
E(−θ− /ρ) instead of E(−θ+ )/E(ρ) and E(−θ− )/E(ρ). For further discussion, see Pesaran and Smith (1995).
10
the mean of the corresponding time-series per country and then form an above-the-mean and
a below-the-mean group of countries. Figure 1 shows the country rankings, while Appendix C
reports the pairwise correlations among the candidate economic variables.
[Insert Figure 1 about here]
The rankings confirm well-known facts; e.g., Argentina and Brazil exhibit the highest inflation and FX volatility while Switzerland and the Scandinavian countries have the best GDP per
capita. The net commodity importer indicator (discussed in Section 2) separates out the commodity currency countries (Australia, Canada, Brazil, Chile, New Zealand, Norway and South
Africa) from the major net commodity importers (China, Hong Kong, Japan and the U.S.).
4
4.1
Empirical Results
Country-by-Country Analysis
According to the LOOP for traded goods, the existence of a long-run equilibrium between the
import price pit , export price p∗it , and nominal exchange rate sit , prevents them from drifting too
far apart over prolonged periods. The cointegration test results in Table 2 affirm this wisdom
and hence, support the established practice of employing error correction (or ARDL) models to
analyse ERPT behaviour; see e.g., Delatte and Lopez-Villavicencio (2012), Brun-Aguerre et al.
(2012), Kozluk et al. (2008), Campa et al. (2008)). For the vast majority of countries, at least
one of the two tests described in Section 3.1 (the tBDM or FP SS test) is able to reject the null
hypothesis of ‘no cointegration’ (or no long-run equilibrium), but the evidence weakens when the
restriction of symmetric long-run pass-through, βi+ = βi− , is imposed in the NARDL model. An
important example is the U.S. where the large FP SS statistic of 5.132 provides strong evidence
of an asymmetric long-run relationship but the small FP SS statistic of 2.713 implied from the
symmetric NARDL model provides no evidence of a long-run relationship. This suggests that
imposing long-run symmetry may lead to biases in estimation and inference.
[Insert Table 2 about here]
The estimation results of the NARDL model (3) on a country-by-country basis are reported in
Table 3. We adopt the lag structure p = q = r = 2 for all countries i = 1, ..., N since it generally
suffices to whiten the residuals.13 The models fit the data well in most cases as borne out by
13
Only in two NARDL models, those corresponding to Hungary and Chile, the Ljung-Box portmanteau Q
test suggested residual autocorrelation. However, the use of heteroskedasticity and autocorrelation consistent
(Newey-West ) standard errors did not materially change the inferences on the significance of the pass-through
coefficients in these two cases. Therefore, inferences are based on OLS standard errors for all countries.
11
the degrees-of-freedom adjusted explanatory power R̄2 which ranges from 0.428 (Singapore) to
0.913 (Argentina) for EMs, and from 0.274 (Spain) to 0.786 (Australia) for DMs.
[Insert Table 3 about here]
There is strong evidence of asymmetric long-run pass-through. We find that 18 countries ((7 are
EMs and 11 are DMs) experience asymmetric pass-through over the long run; for all (but one)
of them, the long-run pass-through of depreciations exceeds that of appreciations, i.e. β̂i+ > β̂i− .
The tests cannot reject the null hypothesis of complete long-run pass-through of depreciation
for 19 DMs and 9 EMs, while the corresponding proportions for appreciation are 10 DMs and
7 EMs; thus, the evidence suggests that depreciations are reflected in import prices more often
than depreciations over the long run, particularly when the importing economy is developed.
These findings reveal that, on the aggregate, exporting firms are unwilling or unable to absorb
adverse exchange rate fluctuations into their operating margins over long periods. The longrun asymmetry uncovered by our tests is suggestive of imperfect competition among exporting
firms and of limited pricing power of importers. Indeed, the fact that we observe asymmetry
in the long-run indicates that exporters may be able to maintain pricing power. This would
be feasible if exporters are able to consistently differentiate their products over time by means
of innovation, quality enhancements or technological progress, all supported by appropriate
marketing strategies. Alternatively, our results could arise from either tacit or explicit price
collusion among exporters regarding their response to exchange rate fluctuations.
Turning now attention to impact ERPT, we observe asymmetry for 12 with no obvious
distinction between DMs and EMs; 4 DMs and 3 EMs show stronger impact ERPT following
depreciations, while 2/19 DMs (11%) and 3/14 EMs (21%) show the reverse pattern. Our tests
do not reject the null hypothesis of zero impact ERPT to import prices in the U.S., in line with
previous studies (e.g., Campa and Goldberg, 2005, and Marazzi et al., 2005). For less than half
the total cross-section of countries, we observe significant impact ERPT; for 6 DMs and 8 EMs
we find that depreciations are fully passed on impact, and for 7 DMs and 6 EMs we find that
appreciations are full passed on impact.
Finally, the null hypothesis of zero short-run cumulative ERPT can be rejected for 11 EMs
and 14 DMs in the case of depreciations, and 11 EMs and 16 DMs for appreciations. We find
evidence of cumulative short-run pass-through asymmetry for 10 countries in total with no clear
distinction between EMs and DMs; namely, 2 DMs and 2EMs show stronger short-run ERPT
for depreciation, and 3 DMs and 3 EMs show asymmetry in the opposite direction.
12
Figures 2 and 3 plot the cumulative dynamic multipliers associated with a unit depreciation
−
(m+
i,h ) and appreciation (mi,h ) of the domestic exchange rate for each country in our sample. The
−
differential m+
i,h −mi,h , h = 0, 1, ..., H is shown together with its 90% bootstrap confidence band.
The general tendency for depreciations to be passed-through more strongly than appreciations
over the long run is clearly borne out by the multipliers; short-run asymmetry is far less pervasive.
[Insert Figures 2 and 3 about here]
In various countries the dynamic multipliers exhibit an ‘overshooting’ effect whereby depreciations are passed less strongly in the immediate short run, but gradually the pass-through of
depreciations gains prominence with the horizon; instances are Canada, Japan, the US, Hong
Kong, and Singapore. This group of countries includes some of the world’s most lucrative export
markets with well-informed and affluent agents that enjoy considerable freedom to trade. Hong
Kong and Singapore have experienced very rapid development over the last decade. Exporting
firms may be more willing to narrow their markups to absorb adverse exchange rate fluctuations
over the short run in order to gain market share in such favourable markets.
Asymmetric pass-through only over the long run is experienced by many DMs such as Australia, Belgium, Denmark, Finland, Sweden, Switzerland and the U.K., as well as South Korea
which is among the most developed of the EMs. Most of these economies are wealthy, relatively
small and with well-established import markets. In this environment, exporting firms may face
very strong competition which deters them from rent-seeking asymmetric pass-through in the
short run. For another group of economies, we observe that depreciations are passed more
strongly than appreciations both over the short- and long-run; notable instances are Argentina,
China, Greece, Israel and Thailand. A common aspect to these markets is that they are either
small or subject to restrictive trade regulations. As such, exporters selling to them may face
a relatively low degree of competition and will therefore be relatively unconstrained in their
ability to engage in short-run rent-seeking behaviour. A final group of countries – Chile, Colombia, Ireland, Italy, Mexico, the Netherlands, New Zealand and South Africa – exhibits roughly
symmetric pass-through both in the short- and long-run. A feature they share is that they are
either net commodity exporters or re-export locations.
4.2
Panel Analysis
Panel pass-through coefficients and hypotheses tests are shown in Table 4 for various groups
of countries. The corresponding cumulative dynamic multipliers are plotted in Figure 4. A
13
result that resonates across country groupings is that the pass-through following depreciations
is generally complete and significantly stronger than that of appreciations over the long run.
Thus, over long horizons and on the aggregate exporting firms are keen to absorb appreciations
by widening their margins while the import price is kept intact, but they pass depreciations
which increases the import price while their margins remain intact.
[Table 4 and Figure 4 around here]
Regarding the long-run ERPT elasticities, we find little evidence of differences across country
groups. For instance, the results for the t-test between groups reported in Table 4 suggest no
distinction between the long-run ERPT elasticities for high versus low inflation economies, or
for high versus low per capita GDP economies. This is corroborated by the cumulative dynamic
multipliers shown in Figure 4. The only exception is the high versus low commodity import
comparison which reveals a significant difference in long-run ERPT elasticities; long-run asymmetry is more pronounced in countries that rely on commodity imports, and the effect derives
from significantly stronger long-run pass-through of depreciations in these countries relative to
the low commodity import countries. This finding indirectly suggests that the import price
of commodities reacts asymmetrically with respect to exchange rate shocks, possibly reflecting
the relatively inelastic nature of commodity demand and the secular trend of increasing global
commodity demand throughout the sample period.
The panel estimation results in Table 4 reveal some evidence of impact asymmetry but no
evidence of short-run cumulative asymmetry for any of the 17 country groups considered. Where
impact asymmetry is significant, it is because the contemporaneous effect of a depreciation on
import prices exceeds that of an appreciation (i.e., π + − π − 6= 0), a pattern which is again
suggestive of weak competition. The impact asymmetry is significant in 6 groups which include
the high commodity import countries and those whose currency is highly volatile.
To sum up, both the time-series and panel analyses reject the hypothesis of symmetric
ERPT over the long run, suggesting that depreciations tend to be passed more vigorously
than appreciations. This is an important finding, because the majority of extant studies have
modelled the long-run equilibrium relationship between exchange rates, import and export prices
as symmetric. Furthermore, our results indicate that once asymmetry in the long-run equilibrium
path is explicitly modeled, the evidence of short-run asymmetry considerably weakens.
14
4.3
Cross-Section Analysis of Long-Run Asymmetric Pass-Through
We now seek to explain the cross-section variation in the long-run ERPT asymmetry by fitting
OLS regressions for the estimated differential measure LRiasy ≡ βi+ − βi− , which is positive for
the large majority of countries, on various factors (discussed in Section 2) that describe different
economic aspects of the importing country. The results are presented in Table 5.
[Table 5 around here]
The first section of the table reports univariate regressions to provide a baseline set of results, although there may be omitted-variable biases. Import dependence stands out as the
most prominent factor; greater import dependence is associated with stronger pass-through for
depreciations than appreciations over the long run. The multivariate regressions affirm the important role of import dependence. To ensure that the inclusion of the rate of inflation and/or
its volatility among the explanatory variables does not contaminate the results with endogeneity
biases, we also report a multivariate regression excluding them.14 The main finding that long-run
pass-through asymmetry is significantly linked to import dependence remains unchallenged.
We explore further the role of import dependence by fitting a nonlinear regression where importing economy enters separately and also interacted with all other explanatory variables; this
model allows for distinct marginal effects of import dependence across countries. The results,
shown in Section C of Table 5, reveal that the positive association between import dependence
and long-run asymmetry is moderated by freedom to trade. Thus, importing economies with
greater freedom to trade internationally provide less room for opportunistic pass-through asymmetry possibly because exporters face stronger competition in these markets. Another significant
interaction occurs between import dependence and the output gap which also serves to moderate
the long-run ERPT asymmetry. The interpretation is that exporters pricing policies factor in
the country-specific stage of the business cycle; in destination markets growing above potential,
exporters may quote more competitive prices in order to gain market share. Lastly, we find no
evidence of differences in the extent of long-run ERPT asymmetry for EMs and DMs.
5
Summary and Policy Implications
A thorough understanding of the extent of exchange rate pass-through into import prices is
central to the analysis of global trade imbalances, the conduct of monetary policy, and the
14
This may arise when depreciations are passed through more strongly than appreciations in the long-run, as
this would be expected to eventually feed into the general price level and therefore, the volatility of inflation.
15
appropriate choice of exchange rate regime. Although the literature on the subject is ample,
no existing study has yet investigated depreciation versus appreciation asymmetry in both the
long-run equilibrium and the short-run dynamics for a large sample of developed and emerging
economies. This paper seeks to fill this gap by estimating nonlinear error-correction models and
dynamic multipliers over different horizons for a large panel of 33 countries.
Our findings suggest that on the whole exporting firms engage in asymmetric pass-through
over the long run. The long-run pass-through is stronger for depreciations than appreciations
and the asymmetry is linked with the degree of import dependence. This evidence is indicative
of weak competition structures in international trade which allow exporting firms to extract
rents from exchange rate fluctuations. Consequently, the response of import prices to currency
changes is upwardly skewed and translates into downward nominal price rigidity and welfare
losses for consumers. This offers a partial explanation for the puzzling observation that, in
many countries during the recession that followed the late 2000s financial crisis, inflation has
not fallen significantly despite anaemic global demand.
Our results raise a general concern about market concentration among exporters as well as
price discrimination between destination markets. These are important issues which will be very
difficult to regulate given their inherently multi-jurisdictional nature. Furthermore, there are
significant implications for the design and conduct of monetary policy, as domestic policies aimed
at controlling inflation and managing aggregate demand may be undermined by asymmetric
responses of import prices to exchange rate fluctuations. Our analysis identifies an important
policy response that could help to mitigate pass-through asymmetry over the long run. Since
the positive link between the long-run asymmetry and import dependence lessens with greater
trade openness, it follows that trade reform and liberalisation policies should be an effective
tool to mitigate opportunistic pass-through behaviour. Finally, the similarity of our results for
both developed and emerging economies provides indirect evidence that the ‘fear of floating’ of
many emerging economies may be unwarranted from an import price perspective. Therefore, by
allowing their exchange rates to float more freely, policymakers in emerging economies would
gain greater freedom to conduct policy in accordance with domestic stabilisation goals.
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19
20
i,t
∗
GDPi,t
, is estimated using the HP filter. GDP per capita is denominated in thousands of nominal US$. The commodity importer and Trade Freedom series are discussed in detail in Section
2. Data sources are indicated by the following abbreviations: G/D/T stands for GSCI/DJ-UBSCI/TR-J CRB; JPM stands for J.P. Morgan; NSO stands for National Statistics Office; ME
stands for Ministry of Economics; CM stands for Commerce Ministry; NCB stands for National Central Bank and Fraser stands for The Fraser Institute. All remaining abreviations are
widely known.
Notes: Countries are identified by two-letter ISO codes. Import and export prices are trade-weighted indices and NEER is the nominal effective exchange rate of foreign currencies per
unit of domestic currency, as detailed in Section 2. To ensure comparability with the dataset used in Brun-Aguerre et al. (2012), additional export price data for Bolivia, India, Indonesia,
M
Pakistan, Russia, and Turkey were used in the creation of the trade-weighted export price variable for each importing economy. Import dependence is computed as IDit ≡ GDPi,ti,t
,
−Xi,t
where Mi,t is the total value of imports,
GDP is nominal output, and Xi,t is the total value of country i’s exports. FX volatility is based on the realised standard deviation of daily foreign
qP
∗
GDPi,t −GDPi,t
N EERj
D
2
exchange returns such that RVitF X ≡
× 100 where trend output,
j=1 [log( N EERj−1 )] , where D is the number of days in each quarter. The output gap is computed as
GDP ∗
APPENDIX A: Data Sources
APPENDIX B: Bootstrap Confidence Bands for Multipliers
The cumulative dynamic multipliers and associated non-parametric bootstrap intervals plotted
in Figures 2 and 3 are computed as follows:
1. Estimate the NARDL model (3) for country i by OLS to obtain the regression residubi =
als, ˆi,t , t = 1, ..., Ti and the vector of relevant pass-through parameter estimates Θ
−
+
+
+
+
−
−
0
(βbi , π
bi,0 , π
bi,1 , βbi , π
bi,0 , π
bi,1 ) from which the cumulative dynamic multipliers mi,h and m−
i,h
are computed. Note that the sample length for country i is denoted Ti to reflect the
differences in the estimation samples across countries detailed in Table 1.
2. Resample from ˆi,t with replacement and denote the vector of resampled residuals for
(b)
country i by ˆi,t , t = 1, ..., Ti .
(b)
3. Generate the bootstrap sample for country i, ∆pi,t , t = 1, ..., Ti , by recursion as follows,
taking the explanatory variables as given:
(b)
∆pi,t
(b)
b− −
b ∗
= α
bi + ρbi pi,t−1 + θbi+ s+
i,t−1 + θi si,t−1 + λi pi,t−1
+
p−1
X
j=1
(b)
ϕ
bi,j ∆pi,t−j
+
q−1 X
+
π
bi,j
∆s+
i,t−j
j=0
+
−
π
bi,j
∆s−
i,t−j
+
r−1
X
(b)
φbi,j ∆p∗i,t−j + εbi,t
j=0
(b)
4. Re-estimate the NARDL model for country i using the bootstrap sample ∆pi,t to obtain
+(b)
+(b)
−(b)
−(b)
−(b)
b (b) = (βb+(b) , π
bi,0 , π
bi,1 , βbi , π
bi,0 , π
bi,1 )0 .
the bootstrap parameter vector Θ
i
i
5. Compute the cumulative dynamic multipliers for country i using the bootstrap parameter
vector.
6. Repeat steps 2–5 B times and compute empirical confidence intervals of any desired width
around the cumulative dynamic multipliers obtained at step 1 in the usual way. Repeat
the process for all countries i = 1, 2, . . . , N .
The panel cumulative dynamic multipliers and the associated confidence bands plotted in Figure
4 can be computed as follows:15
7. Compute the mean of the N country-specific pass-through parameter estimates Θ̄ =
(β̄ + , π̄0+ , π̄1+ , β̄ − , π̄0− , π̄1− )0 from which the panel dynamic multipliers can be obtained.
8. Note that steps 2–4 have already been carried out B times for the full set of countries i =
1, 2, . . . , N . Now, for each bootstrap sample, b = 1, 2, . . . , B, compute the mean of the N
(b)
+(b)
+(b)
−(b)
−(b)
country-specific bootstrap coefficient vectors to obtain Θ̄ = (β̄ +(b) , π̄0 , π̄1 , β̄ −(b) , π̄0 , π̄1 )0 .
9. Compute the cumulative dynamic multipliers for each of the B bootstrap MG parameter
(b)
vectors Θ̄ and then compute empirical confidence intervals of any desired width around
the MG cumulative dynamic multipliers obtained at step 7 in the usual way.
15
Here we discuss the simple case in which the MG estimator averages over the NARDL parameter estimates
for all countries in the sample. Generalisation to the case where one averages over a subset of the countries follows
easily.
21
APPENDIX C: Pairwise Correlations between the Country Drivers
Notes: For each pair of drivers xA and xB , the figures reported represent the estimated correlation ρA,B =
corr(xA,i , xB,i ) for i = 1, 2, . . . , 33 where xA,i represents the time average of driver xA for the ith country.
p-values are reported in parentheses.
22
23
Notes: Depr(+) counts the number of quarters over the sample period during which the exchange rate change is positive; Appr(-) counts the number of quarters when
it is negative. ADF is the Augmented Dickey-Fuller test for the null that the variable is integrated of order one, I(1), against the alternative of I(0) behaviour. KPSS is
the Kwiatkowski-Phillips-Schmidt-Shin test for the I(1) null against the I(0) alternative The ADF and KPSS test equations include both a constant and a linear time
trend. The lag order of the ADF test is selected using the modified AIC criterion, and the results are based on the MacKinnon critical values. The bandwidth of the
KPSS test is based on the Newey-West estimator using the Bartlett kernel and the results are based on the critical values tabulated by KPSS. ∗ , ∗∗ and ∗∗∗ denote
rejection of the null at the 10%, 5% and 1% level, respectively.
Table 1: Descriptive Statistics
Table 2: Cointegration Test Results
Notes: PSS denotes the Pesaran et al. (2001) F -test of the null hypothesis ρ = β + = β − = θ = 0 against
the alternative of joint significance. BDM denotes the Banerjee et al. (1998) t-test of the null hypothesis ρ = 0
against the one-sided alternative ρ < 0. In both cases, the null hypothesis indicates the absence of a long-run
levels relationship. The relevant critical values tabulated by Pesaran et al. for the BDM t-test are -3.21 (10%),
-3.53 (5%) and -4.10 (1%). The equivalent values for the PSS F -test are 4.14 (10%), 4.85 (5%) and 6.36 (1%). ∗ ,
∗∗
and ∗∗∗ denote rejection of the null at the 10%, 5% and 1% level, respectively.
24
25
Notes: Data span is the effective sample used in estimation after adjusting for lags and first differences. β + (β − ) is the long-run pass-through associated with a
depreciation (appreciation). π0+ (π0− ) is the contemporaneous or impact pass-through reflected in the same quarter as the exchange rate shock. π1+ (π1− ) is the short-run
ERPT one quarter after the exchange rate shock. Where a coefficient is printed in bold face it is statistically greater than or equal to unity (indicating full pass-through)
at the 5% level. ∗ , ∗∗ and ∗∗∗ denote statistical significance at the 10%, 5% or 1% levels. Inferences are based on OLS standard errors.
Table 3: Individual Pass-Through Estimation Results
26
Notes: In all cases, the ‘low’ and ‘high’ cohorts include 17 and 16 countries, respectively. These cohorts are selected by ranking the countries in the sample according
to the corresponding economic criteria as shown in Figure 1. The panel estimates are obtained by applying the Mean Group estimator to the country-specific NARDL
MG
models in 3 and inferences are based on the Mean Group covariance matrix V (Θ̄
). The column labelled “t-test between groups” reports standard t-statistics for the
equality of the panel coefficients in the relevant groups outlined in the first column.
Table 4: Panel Pass-Through Estimation Results
27
Notes: The number of observations is N =33. The dependent variable is the degree of long-run asymmetry estimated as βbi+ − βbi− for country i, where the βi ’s are the
asymmetric long-run parameters from the NARDL models for countries i = 1, 2, . . . N . The regressors include a dummy variable (Emerging, which is equal to 1 for
EMs and 0 for DMs) and economic variables which are entered as time-averages over the longest common time period for all countries. Inferences are based on OLS
standard errors which are reported in parentheses since the Breusch-Pagan-Godfrey heteroskedasticity test was insignificant at all standard levels in all cases.
Table 5: Analysis of Country Variation in Long-Run Pass-Through Asymmetry
(a) Import Dependence
(d) GDP per capita (US$, thousands)
(g) Size FX Change
0.0
0.1
0.2
‐1
0
1
2
3
4
5
6
7
8
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0
1
2
3
4
5
6
7
8
(b) FX Rate Volatility
(e) Commodity Importer
(h) Inflation Rate
0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
9
10
‐0.6
‐0.5
‐0.4
‐0.3
‐0.2
‐0.1
0.0
0.1
0.2
(c) Output Gap
(f) Trade Freedom
(i) Inflation Volatility
Notes: Each figure ranks the countries in our sample according to the average value of the named driver over the largest common time period for all countries,
1980Q1–2010Q4. In each case, the countries are partitioned into two groups which are identified by red/blue shading.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0
2
4
6
8
10
12
14
16
‐6
‐4
‐2
0
2
4
6
8
Figure 1: Country Rankings According to Selected Importer Characteristics (Average Values, 1980Q1–2010Q4)
HK
SG
BR
JP
US
CO
AR
AU
GR
UK
IT
NO
ES
FR
NZ
ZA
CN
MX
CL
DK
IL
DE
FI
CA
SE
CH
KR
IE
TH
CZ
NL
HU
BELU
CN
TH
CO
BR
AR
CL
MX
HU
CZ
KR
ZA
GR
IL
NZ
ES
HK
IT
SG
DE
CA
FR
BELU
AU
UK
JP
FI
NL
SE
US
IE
DK
CH
NO
BR
AR
MX
ZA
CN
KR
CL
JP
CO
AU
TH
NZ
HU
GR
IL
UK
SE
US
CZ
CA
FI
CH
NO
IT
IE
HK
ES
DE
SG
FR
DK
NL
BELU
HK
DK
SG
CN
US
BELU
DE
NL
ES
IT
FR
FI
DE
CH
IE
TH
CZ
UK
NO
SE
IL
CA
HU
MX
CL
KR
CO
JP
AU
NZ
AR
ZA
BR
BR
AU
KR
CL
MX
CO
ZA
CA
HU
SE
NZ
NO
UK
CZ
IL
IE
SG
IT
ES
GR
FR
NL
DE
BELU
FI
DK
TH
CH
AR
HK
CN
JP
US
HK
JP
CH
SE
SG
DE
FR
FI
CN
UK
NL
BELU
CA
NO
DK
IT
IL
TH
NZ
US
CZ
IE
ES
KR
AU
CL
GR
ZA
MX
CO
HU
BR
AR
AR
CO
CL
TH
HK
CN
SG
BR
JP
CZ
HU
ZA
NO
BELU
NZ
DE
SE
UK
DK
KR
IT
FI
AU
MX
IL
US
CH
GR
FR
CA
IE
ES
NL
CN
CO
ZA
AR
TH
KR
BR
MX
JP
GR
AU
NO
CZ
HU
IT
ES
FR
FI
CH
CA
SE
US
CL
DE
BELU
NZ
IE
DK
IL
NL
UK
SG
HK
DK
IT
DE
CH
FR
JP
CA
UK
NL
GR
KR
NZ
NO
SE
BELU
AU
ES
US
FI
CZ
SG
TH
HU
CL
CO
IL
CN
HK
BR
IE
ZA
MX
AR
28
29
-0.5
-1.0
-1.5
-0.5
-1.0
-1.5
-0.8
-0.4
0.0
0.4
0.8
1.2
(l) Netherlands
(c) Canada
(h) Greece
-1.0
-0.5
0.0
0.5
1.0
0.0
-.8
-.4
.0
.4
.8
-1.5
-1.0
-0.5
-1.5
-1.0
-0.5
0.0
0.5
1.0
(n) Norway
(i) Ireland
(d) Denmark
(r) United Kingdom
(m) New Zealand
(q) Switzerland
-1.0
-0.5
0.0
0.5
1.0
-0.5
0.0
0.5
0.5
1.0
1.5
-1.0
-0.5
0.0
0.5
(s) United States
-1
0
1
2
-3
-2
-1
0
1
-0.8
-0.4
0.0
0.4
0.8
1.2
(o) Spain
(j) Italy
(e) Finland
Notes: The solid (long-dashed) black line shows the cumulative dynamic multiplier effect of a one percent depreciation (appreciation) of the exchange rate on the import
price, measured in percentage points on the vertical axis. The heavy short-dashed blue line depicts the difference between these two cumulative dynamic multipliers
(i.e. it is computed as a linear combination of the solid and dashed black lines) while the shaded region reports its 95% bootstrap confidence interval. Tick marks on
the horizontal indicate quarterly intervals over a 24 quarter horizon.
-.8
-.4
.0
(p) Sweden
0.0
0.0
.4
0.5
-0.8
1.0
.8
0.0
-0.4
(g) Germany
1.5
0.8
1.0
2.0
0.4
2.5
-1.2
-0.8
1.2
(b) Belgium
0.0
-0.4
1.0
1.5
0.8
0.4
2.0
1.2
1.6
0.5
(k) Japan
(f) France
(a) Australia
-2
-1
0
1
2
3
1.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
-.8
-.4
.0
.4
.8
Figure 2: Cumulative Dynamic Multipliers from the Unrestricted NARDL(2,2,2) Model (Equation 3) – Developed Markets
30
-1.0
-0.4
-3
-2
-1
0
1
(k) Singapore
-1.0
-0.5
0.0
0.5
0.4
0.4
-0.8
-0.4
-0.8
-0.4
0.0
0.8
0.0
1.2
(h) Hungary
0.8
(l) South Africa
-1.5
-1.0
-0.5
0.0
0.5
1.0
(c) Chile
1.2
(g) Hong Kong
(b) Brazil
-1.0
-0.5
0.0
0.5
1.0
1.5
(m) South Korea
-1.0
-0.5
0.0
0.5
1.0
1.5
-1.0
-0.5
0.0
0.5
1.0
(i) Israel
(d) China
-1
0
1
2
(n) Thailand
-1.0
-0.5
0.0
0.5
1.0
-1.0
-0.5
0.0
0.5
1.0
(j) Mexico
(e) Colombia
Notes: The solid (long-dashed) black line shows the cumulative dynamic multiplier effect of a one percent depreciation (appreciation) of the exchange rate on the import
price, measured in percentage points on the vertical axis. The heavy short-dashed blue line depicts the difference between these two cumulative dynamic multipliers
(i.e. it is computed as a linear combination of the solid and dashed black lines) while the shaded region reports its 95% bootstrap confidence interval. Tick marks on
the horizontal indicate quarterly intervals over a 24 quarter horizon.
-1.2
-0.8
-0.4
0.0
0.4
(f) Czech Rep.
-0.5
0.0
1.0
0.0
0.4
0.8
0.5
0.8
(a) Argentina
1.0
1.2
Figure 3: Cumulative Dynamic Multipliers from the Unrestricted NARDL(2,2,2) Model (Equation 3) – Emerging Markets
31
(q) Comm. Importer
-0.8
-0.4
0.0
(r) Trade Freedom Low
-0.8
-0.4
0.0
0.4
0.4
(l) Output Gap Low
(f) Infl. Vol. Low
(b) DMs
(c) EMs
(s) Trade Freedom High
-0.8
-0.4
0.0
0.4
0.8
1.2
(m) Output Gap High
-1.0
-0.5
0.0
0.5
1.0
(g) Infl. Vol. High
-1.0
-0.5
0.0
0.5
1.0
-1.0
-0.5
0.0
0.5
1.0
(t) Size ∆FX Low
-0.8
-0.4
0.0
0.4
0.8
1.2
(n) GDP p/c Low
-1.0
-0.5
0.0
0.5
1.0
(h) Import Dep. Low
-0.8
-0.4
0.0
0.4
0.8
1.2
(u) Size ∆FX High
-1.0
-0.5
0.0
0.5
1.0
(o) GDP p/c High
-0.8
-0.4
0.0
0.4
0.8
1.2
(i) Import Dep. High
-0.8
-0.4
0.0
0.4
0.8
1.2
Notes: The solid (long-dashed) black line shows the cumulative dynamic multiplier effect of a one percent depreciation (appreciation) of the exchange rate on the import
price, measured in percentage points on the vertical axis. The heavy short-dashed blue line depicts the difference between these two cumulative dynamic multipliers
(i.e. it is computed as a linear combination of the solid and dashed black lines) while the shaded region reports its 95% bootstrap confidence interval. Tick marks on
the horizontal indicate quarterly intervals over a 24 quarter horizon.
(p) Comm. Exporter
-1.0
-0.5
0.0
0.5
(k) FX Vol. High
-1.0
0.8
-0.8
-0.8
1.2
-0.4
-0.4
-0.5
0.8
0.0
0.0
0.0
0.5
1.0
-0.8
-0.4
0.0
0.4
0.8
1.2
-0.8
-0.4
0.0
0.4
0.8
1.2
1.2
0.4
0.4
1.0
0.8
(j) FX Vol. Low
1.2
0.8
(e) Infl. Rate High
-1.0
-0.5
0.0
0.5
1.0
(a) All Countries
1.2
(d) Infl. Rate Low
-0.8
-0.4
0.0
0.4
0.8
1.2
-1.0
-0.5
0.0
0.5
1.0
Figure 4: Cumulative Dynamic Multipliers for Selected Country-Groups Computed by Mean-Group Estimation