İqtisadi Tədqiqatlar Mərkəzi
Alternative inflation rate and
model development project
Research Report
By Gubad Ibadoglu,
Economic Research Center,
Caspian Plaza, May 19, 2010
1 2 3 4 5
PRESENTATION CONTENT
•
•
•
•
•
•
Research agenda
Survey methodology
Alternative inflation results
Determinants of inflation
Forecasting
Conclusions.
Economic Research Center
2
1 2 3 4 5
PROJECT OVERVIEW
NAME OF THE PROJECT:
• Alternative Inflation Rate and Model Development
GOALS & OBJECTIVES
• The project proposed here includes the calculation of Monthly Consumer
Price Indices using the methodologies and weights derived in the preceding
project. The products of this project will be
• determination of the most appropriate calculation and forecasting methods
for inflation,
• preparation of an alternative inflation model for use by government
agencies,
• publication through a media campaign
• popularization of the “family inflation calculator”( www.inf.erc-az.org) in
the center’s web-site that can be used by families themselves to calculate
inflation rate for their own families.
Economic Research Center
3
1 2 3 4 5
METHODOLOGY
RESEARCH APPROACH:
Theoretical
Framework
Survey of
22 districts
PROJECT DESIGN
• Testing for New Keynesian
Phillips Curve, ARIMA, Restricted
and Unrestricted VAR;
• A combination of random,
systematic and stratified sampling;
• Defining preliminary sampling
units, the distribution of
respondents among regions,
• and a stratification based on the
size of the firm;
• Training sessions for survey
conductors;
• Administering the survey over 9
economic regions;
• Entering survey results in machinereadable form;
• Analyzing the findings of the
survey;
• Preparing final research report.
Economic Research Center
4
In the framework
of the project:
•
Inflation rate is
calculated every
month, on the
basis of the
information on
51 480 prices
•
The survey covers
9 economic
regions of
Azerbaijan
•
The survey is
carried out in 22
cities/towns and
districts
•
Price monitoring
includes a range
of 586 products
5
1 2 3 4 5
Research design
I.
Alternative
inflation
results
II.
Determinant
s of inflation
III.
Forecasting
Economic Research Center
6
1
I. Consumer Price Index
3
Monthly inflation
1.9
2
2
1.35
1.5
1.15
1.06
1
0.7
0.65
0.49
0.5
0.1
0.02
0.03
0.03
0
jan
-0.05
mar
feb
inflation
Food
non-food
4.48
4.47
4
-0.19
service
Cumulative inflation
5
3.28
3
2.47
2.44
1.9
1
apr
-0.32
-0.5
2
0.23
0.01
1.72
1.06
0.1
0.02
0.54
0.05
0
jan
feb
-0.22
-0.27
mar
-0.05
apr
-1
inflation
food
non-food
0.35
service
According to the information
released by State Statistics
Committee, inflation for first
four months were 0.5, 1.1, 1.3
and 0.0 percent respectively.
4
5
Consumer Price Index
10.14
12
10
6
6.82
6.41
5.98
4.78
11.07
8.31
8.01
8
4
11.23
10.81
1 2 3 4 5
CPI compared to the corresponding
month of the previous year
3.25 3.34
2
0
-2
-4
jan
-0.67
mar
feb
-2.39
-2.34
inflation
food
10.14
12
-0.32
apr
non-food
CPI compared to the corresponding
period of the previous year
service
10.82
10.73
10.47
10
8
6
4
3.25 3.34
4.02
4.66
5.78
4.81
5.31
6.41
2
0
-2
-4
jan
feb
-2.34
mar
-1.8
apr
-1.43
-2.36
inflation
food
non-food
service
According to the information
released by State Statistics
Committee, inflation for
respective four periods were
1.8, 2.8, 3.8 and 4 percent
respectively.
1 2 3 4 5
Minimum consumer basket (MCB)
Price index of MCB
1.40%
1.33%
1.20%
1.00%
0.91%
0.80%
0.67%
0.53%
0.60%
0.35%
0.40%
0.41%
0.20%
0.00%
0.03%
0.08%
0.08%
feb
mar
apr
labor force
retired
children
Cost of MCB for different consumer baskets
months
labor force
retired
children
January
117,82
105,47
136,68
February
118,23
106,03
137,24
March
119,8
106,99
138,16
April
119,84
107,08
138,27
With the decree of the
Parliament, the living
minimum wage is set to 87
AZN, 96 AZN for labor
force, 68 AZN for retired
and 72 AZN for children.
1 2 3 4 5
II. Methodology of the research
on CPI’s determinants
The vector autoregressive (VAR) model is commonly
used for forecasting systems of interrelated time series
and for analyzing the dynamic impact of random
disturbances on the system of variables. The VAR
approach sidesteps the need for structural modeling
by treating every endogenous variable in the system
as a function of the lagged values of all of the
endogenous variables in the system.
Structure of the Research
• Using monthly data from January, 2000 –
December, 2009 we estimated a VAR(5)and
VAR(1) for the I(0) variables CPI, exchange rate,
price of industrial products and M2. The output
is large because involves estimating 4 equations
with 5 lags for each variable, i.e. 20 parameters
each plus the constant: 21x4=84 parameters.
Research results
• It can be noted that CPI depends on previous CPI data and as
deep as lag extend the dependence decreases. For example, 1
point growth in CPI in a lag before causes 1.6 point growth in
CPI.
• But exchange rate of AZN negatively impact on CPI and 1
percent growth in exchange rate in a lag before diminishes
CPI 0.66 percent. As lag moves deeper dependence rate
between CPI and exchange rate fluctuates.
• It is notable that relation between M2 and CPI embodies in
our equation so lame. 1 unit increase in money supply
accelerate the CPI 0.003 unit.
• The price of industrial products has certain influence on CPI.
For instance, 1 unit growth of industrial index cause CPI to
move up 0.03 unit.
Determinants of CPI
Price index
of industrial
products
Exchange
rate
CPI
Money
Supply
CPI – A lag
before
Positive impact
Negative impact
III. Forecasting Methodology
• New Keynesian Phillips Curve
• ARIMA
• Restricted and Unrestricted VAR
1 2 3 4 5
Classical, New and Hybrid Phillips curves
for Azerbaijan (2003-2009)
Classical Phillips Curve
New Phillips Curve
Hybrid Phillips Curve
Variables
inflation
Lagged inflation
(significant)
Unemployment rate
(insignificant)
-
-
Lagged inflation
(significant)
Output gap (lags)
(insignificant)
-
-
-
Inflation expectation
(significant)
Output gap (insignificant)
-
-
Inflation expectation
(significant)
Unit labor cost (insignificant)
-
-
-
Inflation expectation
(significant)
Lagged inflation (significant)
Unit labor cost
(insignificant)
Classical, New and Hybrid Phillips curves
for Azerbaijan (2003-2009)
•
•
•
Classical Phillips Curve
New Phillips Curve
Hybrid Phillips Curve
1 2 3 4 5

Classical Phillips Curve

The estimation of classical Phillips curve shows that there seems no
tradeoff between inflation and unemployment as the latter is
statistically insignificant

Replacing unemployment with lagged values of output gap also
emphasize the irrelevance of gap measure in causing inflation
pressure.

New Phillips Curve
o
Following Gali and Gertler (1999) and Gali and Lopez-Salido
(2001), we also estimated New Keynesian Phillips curve with
inflation expectation and gap measure. Here as well, the output gap
is appeared to be insignificant.
o
We also run regression with inflation expectation and unit labor cost
in place of marginal cost as in the original New Keynesian Phillips
curve. It is also interesting that marginal cost measure does not
exert inflationary pressure.

Hybrid Phillips Curve
o
The estimation of Hybrid Phillips curve is also undertaken. As in
the previous cases, though inflation expectation and lagged inflation
are meaningful in explaining inflation, the marginal cost measure
turned to be insignificant
Implications
1 2 3 4 5
 Above listed output results obviously show that both inflation persistence
and inflation expectation are significant factors in the determination of
inflation. It is worth to note that they enjoy the similar power in the
formation of inflation.
 The estimation results put forward that the output gap is not a significant
factor in the shaping of inflation.
 This result has various possible explanations: (i) the output gap measured
uses GDP which excludes imports though inflation measure is calculated
using CPI which includes imports. It seems that inflation springs from price
movements of imported goods. It appears as a plausible explanation for the
imported goods enjoy higher share in the consumer basket (ii) another
explanation might be based on the institutional factors, market structure and
monopolist behavior prevalent in the market.
 Though Phillips curve are frequently appealed to forecast future inflation, it
is not practicable in our case as the estimation output provides evidence
against the existence of significant Phillips Curve relation.
1 2 3 4 5
Using ARIMA to Forecast Inflation
 One of the forecast methods of inflation is to invoke Box-Jenkins
methodology.
 In this literature, the appropriate data generating process (DGP) of
the variable of interest is determined. That is, either AR or MA or
ARMA.
 When DGP of the variable of interest is examined during 2005-2009,
the most appropriate form is AR (1) process for inflation.
π t =0.015+0.62π t-1 -0.021sd 06 -0.017sd 07 -0.014sd 08
Note that represents inflation, sd shows the respective seasonal dummy.
1 2 3 4 5
Theoretical Framework for Forecasting
Model assumes three markets in the economy: goods and services, foreign
exchange and money markets. Economy is in equilibrium if all three
markets clear.
 Aggregate demand for goods and is a function of the real money
balances, the nominal exchange rate. It is assumed that the goods
market is always in equilibrium.
 In the foreign exchange market, flow demand for foreign exchange is a
function of real exchange rate and real income. Foreign financing is
exogenously given and as real income is fixed at the level of aggregate
supply, the real exchange rate movements determine equilibrium in the
market.
 Money demand is assumed to be a function of real income and money
supply is given exogenously. Since real income and money supply is
exogenous, real money balances ensures equilibrium in the market.
If the foreign exchange and money market are in equilibrium, goods and
services market will also clear by the application of Walras law.
1 2 3 4 5
An Unrestricted Vector Autoregressive
Model
A VAR allows a more model-based approach that should be better able
to identify shocks that may trigger turning points in inflation.
In the formulation of inflation model in Azerbaijan, each variable –
prices (CPI), narrow money (M2) and nominal effective exchange rate
(ER), non-oil real GDP- is treated symmetrically This structure of the
system allows for feedback among variables, that is, past values of
each variable are incorporated in each equation describing the data
generating process (DGP) of a time series.
 cpit   0.006   0.585
 m   0.0085   0.676
t



 neert   0.015 0.069

 
 
rngdp
0
.
152
t

 0.258

0.030  0.076 0.012   cpit 1   u cpi ,t 


0.574  0.201 0.014   mt 1   u m ,t 

0.056  0.230  0.013  neert 1   u neer ,t 

 

 0.055 0.606  0.469 rngdp t 1  u rngdp,t 
An Restricted Vector Autoregressive Model
Sequential elimination of regressors (SER) strategy is used which sequentially deletes
those regressors which lead to the largest reduction of the given criterion until no further
reduction is possible.
 CPI t   0.005   0.560

 
 
M
0.086
t


 
 NEERt        

 
 
 RNGDPt   0.130     
0.042         CPI t 1   ucpi ,t 

 

0.659         M t 1   um ,t 

   0.228      NEERt 1   uneer ,t 

 

      0.421   RNGDPt 1  urngdp ,t 
The structural equation for inflation can be shown separately as follows:
CPI t  0.005  0.560CPI t 1  0.042M t 1  0.005s1  0.009s6  0.015s7  0.009s8  ucpi ,t
where denotes the seasonal dummy for the ith month. Subset model only reveals the
significant seasonal effect during summer months and in the beginning of the year.
Forecasting Results
Monthly inflation forecast in 2010, %
2.30%
1.80%
1.30%
0.80%
0.67%
0.32%
0.30%
-0.20%
0.07%
Jan
Feb
March
Apr
May
June
July
Aug
Sept
Oct
Nov
-0.70%
-1.20%
-1.70%
Seasonal factor
Inflation
Deseasonalized
Dec
Inflation forecasting results*
Period
(cumulative)
ARIMA
Unrestricted VAR
Restricted VAR
Iq
2.2
3,76
3.97
IIq
2.7
3.6
5.2
IIIq
3.14
3.54
6.33
IVq
7.01
7.92
10.05
*Weighted average inflation forecast for the end of 2010 is 8.3%
IV. Conclusion
1 2 3 4 5
• According to the model the monetary policy
has low effect on CPI;
• Inflation expectation is the most influential
factor on CPI;
• Fixed exchange rate is the subject to be
discussed;
• Weighted average inflation forecast for the
end of 2010 is 8.3%.
1 2 3 4 5
THANK YOU
ECONOMIC RESEARCH CENTER
Baku, Azerbaijan AZ1065
Jafar Jabbarli 44, Caspian Plaza 3, floor 9
Phone: (+99412) 437 32 30
Fax: (+99412) 437 32 40
Orta
E-mail:
çəkili göstəriciyə
[email protected]
görə 2010-cu ildə 8,3 faiz inflyasiya olacaq.
www.erc-az.org
Economic Research Center
25