Final Thesis on the Determinants of Inflation in Pakistan
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Transcript of Final Thesis on the Determinants of Inflation in Pakistan
CHAPTER NO 1
INTRODUCTION
1
Abstract
This study attempts to investigate the effects of import price and participated money supply on
inflation in Pakistan. The model is estimated for the period 1976 to 2005 on a yearly basis. This
paper employs the cointegration and error correction model (ECM) methodology to estimate the
long-run relationship between the import price, money supply and inflation in Pakistan.
Keywords: Inflation, Money Supply and Import Price.
2
Chapter 1
Introduction
Our study will be focused at the various aspects of inflation in Pakistan from a local and global viewpoint.
Pakistan remained prominent figure among the few developing countries which attained remarkable
economic growth and poverty reduction in the first forty years of its existence. GDP remained up to 6
percent per annum and poverty incidence lowered from 46 percent to 18 percent until late 1980. Inflation
rate remained low and despite high population, growth per capita incomes had almost doubled. The
decade of 1990 was not proved a favorable for the economy of Pakistan and the whole situation was
reversed. Growth rate was lowered to an average 3 to 4 percent and poverty increased to 33 percent of the
population. Inflation was in double digits. Inflation can be defined as the persistent rise in the general
price level across the economy over time. Mild inflation is considered to be desirable for economic
growth. However, high and variable inflation, in general, leads to uncertainties in income and expenditure
decisions of the different groups of the society. The present study is a modest attempt to answer these
questions. The basic objective of the study is to identify the factors influencing inflation in Pakistan. It
includes the following specific objectives:
To review major models of inflation and identify the appropriate model for Pakistan.
To identify the short-term and long term relationship of inflation with the factors after their
identifications; and
To analyze the impact of determining variables in inflation.
3
Inflation or price inflation is a rise in the general level of prices of goods and services in an economy over
a period of time. Inflation can be controlled by Monetary Measures. Several supply side and demand side
factors could be responsible for this surge in inflation. Inflation can be a result of shocks to the supply of
certain food items and to world oil markets. Rising oil prices can fake risk of increase in prices of almost
all other commodities of the consumer case. Such supply-side shocks are very unstable and can cause
large fluctuations in food and oil prices. The effects of this on overall inflation at times can be so
excessive that these cannot be countered through demand management, including monetary policy.
However, greater emphasis in the recent debate on inflation remained on the demand side factors. So,
inflation is an important economic indicator. There are various indicators which measure the inflation,
such as; import price; money supply; gross domestic product (GDP) deflator and so on. But I took two
variables import price and money supply in Pakistan.
.
Definition:
1.1 Inflation
“An increase in the prices of goods and services that is generally expressed as an annual
percentage increase in the Consumer Price Index, as compiled by the Department of Labor”
“ Inflation is a rise the general level of prices and services in an economy over a period of time”
1.2 Money Supply
“Money Supply or money is the total amount of money avaible in an economy at a particular
point in time it usually include currency in circulation and demand deposits.”
4
1.3 Import Price
“An import price index measures changes in the prices of imports of merchandise into a country.
The index numbers for each reference period relate to prices of imports landed into the country
during the period”.
1.4 Causes of inflation in Pakistan:
Press information department of Pakistan in 2005 state some causes of high inflation in Pakistan
including:
Political instability.
Higher duties and taxes.
Decelerating economic growth.
Loose monetary polices.
1.5 Purpose of the study
The purpose of this study is will be to test how inflation is effected by the import price and
money supply. If Money Supply and Import Price are increase then it will directly effect the
inflation. The data of all above variables is taken from the World Develop Indicter (WDI), which
is a publication of international monetary fund (IMF), from publications of word bank, annual
economics surveys by the government of Pakistan and the publication of State Bank of Pakistan.
5
1.6 Research Question and Hypothesis:
Main question:
What is the relationship between Inflation, import price and Money Supply?
Sub questions:
Relationship between Inflation and Import Price.
Relationship between Inflation and Money Supply.
1.7 Hypothesis:
Ho: There is no Relationship between Inflation, Money Supply, GDP Growth and Import Price.
H1: There is Relationship between Inflation, Money Supply, GDP Growth and Import Price.
6
CHAPTER NO 2
Literature review
7
Chapter 2
Literature review
Abdul Aleem et.al (2007) focuses on the identification of the main determinants of recent inflation trends
using the data from the 1972 to 2005 period. The study evaluates the role of different factors such as
government sector borrowing, demand relative to supply, private sector credit, imported inflation,
exchange rate, total tax revenue of the government, adaptive inflation expectations and wheat support
price in explaining inflation. By applying ordinary least square method and verifying results through
Breusch-Godfrey Serial Correlation LM and Augmented Dickey-Fuller tests, it is found that to overall
impact of fiscal policies on inflation was not significant and rather the direct part of taxes was dominant
in putting downward pressure on prices. So on the basis of our analysis, it can be safely stated that
expansionary monetary policy not only contribute in promising GDP growth but it also led to the rise in
consumer prices.
Wasim Shahid (2006) investigated “Why has inflation been high in some of the periods?”For this
purpose he used the data for the period1975:03 to 2003:02. Taken from International Financial Statistics
(IFS) and that on real GDP from Kemal and Arby (2004).There are at least three possible reasons for it;
monetary policy actions, supply side factors and trading partner countries. By using Near-VAR approach
and ordinary least square (OLS) method we draw result from the study that inflation responds positively
to monetary shocks. It means money is an important determinant of inflation in Pakistan. This result is in
line with that of Khan and Schimmelpfennig (2006). Also the study suggests that there is a need for a
study that investigates the determinants of inflation variability by considering all the three factors
discussed above.
Qasim et.al (1996) conducts a study in which the on the whole inflation equation and its two significant
components that are CPI food price inflation and CPI non-food price inflation equations. The cause for 8
disaggregating of overall inflation is that an aggregate inflation may cover the important information
about the factors influencing current rise in inflation. They originate a defect in previous studies that is
the consideration of only non-stationary properties of the related variables and regression of non-
stationary variables can reason superior regression problem so they have used affirmative technique
which is the better indicator of inflation than regression. A time series data from 1971 to 1994 is used to
estimate the equation. Co integration and error correction model are used to estimate the various price
equation using non-stationary properties of variables. Co integration model results suggest that the
general price level depend upon money supply domestic economic activity import price index and interest
rate. Dickey-Fuller and the Augmented Dickey-fuller unit root tests are used to determine the order of
integration of all the variables and all series of data are found to be stationary in first difference. A co
integration regression is estimated by using ordinary least squares. The result of multivariate regression of
overall inflation suggests that there is a strong role of money supply and frequent currency devaluation in
accelerating the inflation in Pakistan. So it can be concluded that there is a strong need for tightening of
the fiscal policy and reviving commodity-producing sectors, fiscal regulation and excess in raising prices
are the key to the success to control the expansion in the overall inflation rate in Pakistan.
Anjum et.al (1996) described the sustainability of fiscal policy in Pakistan and work out the sustainable
level of the deficit keeping stable economic growth, optimal inflation and interest on foreign loans in
view. Former studies have discussed the impact of fiscal deficits on economy but didn’t analyze it in
macroeconomics framework. The data from three time periods: the 1980’s, 1985 and 1993 is used and the
average of sustainable deficits of these time periods is estimated in alternative scenarios. An integrated
model is developed using different debt strategies to formulate alternative scenarios for fiscal deficits.
Required deficit reduction can be obtained based on sustainable rate of inflation, interest rate on loans and
by using prudent debt strategies (domestic and foreign). The model is used to find out the consistency
between fiscal deficits, output growth rate of inflation and other macroeconomics variables. The model is
estimated by using different cases that are developed by combining different variables alternatively to
9
highlight different perspectives. Using these cases the sustainable deficits during the above mentioned
time periods are analyzed. It can be concluded that fiscal deficit need to be reduced for sustainability of
the fiscal system and for stable economic growth.
Kennedy et.al (2003) determine the key determinants of inflation in Ghana to know whether inflation is
a real sector issue or a monetary issue. The different theories proposed by different economists are
classified into two wide categories, the excess–demand theories in the category of expectations-
augmented Phillips curve and the cost-push theories which are currently termed
structuralisms/institutional theories of inflation. They used the data from 1970 to 2002.The excess-
demand theories argued that excess demand for goods and services over supply in an economy is the main
source of inflation. The technique that have been used to estimated the result was regression analysis
which sagest that the explanatory variables was not significant as some of the determining inflation
factors have not been included in the model.
Qayyum (2006) conduct a study in which he found that the monetarist’s proposition that money supply
has been the key determinant of inflation in Pakistan. He estimated the relationship between the rate of
inflation, money growth, growth in real income and growth in velocity in Pakistan. He used the data from
1960 to 2005. The result from the correlation analysis indicates that there is strong relationship between
the money growth and inflation. The correlation coefficient between the money growth and current real
GDP growth is 0.226 and its is 0.069 with previous year’s money growth and current year’s real GDP
growth. However it is statistically insignificant. The results lead us to say that money growth at first
round effect real GDP growth and at second round the money growth effects inflation in Pakistan.
Safdar.A & Omer.F (2009) investigate the effects of political instability on inflation in Pakistan. The
annual time series data has been used for the years 1951 to 2007 which generally covers the economic
and political environment of Pakistan. They recommend two different estimable models the first model
was the summary of the empirical evidence previously available on Pakistan economy and the second
10
model stems from the ‘nonmonetary’ determinants of inflation literature. The dependant variable was
Inflation as the yearly growth rate of Consumer Price Index and to account for the historical impact of
inflation, inflation inertia, as one of the explanatory variables used one period lagged inflation. The
results of the ‘monetary’ model suggest that the effects of monetary determinants were rather marginal
and that they depend upon the political environment of Pakistan. The ‘nonmonetary’ model’s findings
explicitly establish a positive association between measures of political instability and inflation and it was
further confirmed on analyses based on interactive dummies that reveal political instability significantly
leading to high (above average) inflation.
Harris et.al (2004) found the work on financial development has brought a focus to what role it may
play in economic growth, focusing mainly on developed countries. Growth has also been explained in
terms of a negative effect from inflation. The relationship between inflation and growth with conflict in
empirical results. A candidate explanation for such difference can be categorized into to three reasons.
First, the econometric specification have often neglected the very important cross county unobserved
heterogeneity, and also have lacked time effect that account for trend-deviation. Secondly, the non-
linearity and parsimonious specification arising out of a single, comprehensive, theoretical growth model
has no taken into account. This paper presents an endogenous growth model that implies both a
signification, negative, non-linear inflation effect and an appropriate econometric specification. Third the
instruments to account for a possible endogenously of the inflation rate typically have not been
theoretically derived from the same self-continued model. They used the data from 1990 to 2002 for 13
transition countries on inflation, financial development and growth. Two models are specified. First the
Dawson model is presented. This includes the investment rate and financial depth but not inflation. The
second model is extended to include inflation plus other variables as related to a theory of endogenous
growth. They used of the money supply as the instrument is a theoretically given choice and it result in a
significant, non-linear, inflation growth effect in both OECD and APEC samples
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Robert.J (1960-1990) shows the relationship between the inflation and economic growth. He collected
the sample for around 100 countries while the sample period was from 1960 to 1990 to evaluate the effect
of inflation. The Annual inflation rates were computed from consumer price indices. The statistical test
that has been applied on the data was a system of regression equations in which many other determinants
of growth were held constant and the findings revealed that a likely channel by which inflation decreases
growth was through a reduction in the propensity to invest and that the estimated effects of inflation on
growth and investment were significantly negative when some plausible instruments were used in the
statistical procedures thus, there was some reason to believe that the relations reflect causation from
higher long-term inflation to reduced growth and investment. The researcher suggest that if a number of
country characteristics are held constant, then regression results indicate that an increase in average
inflation of ten percentage points per year reduces the growth rate of real per capita GDP by 0.2–0.3
percentage points per year and lowers the ratio of investment to GDP by 0.4–0.6 percentage points
although the adverse influence of inflation on growth looks small, the long-term effects on standards of
living are substantial.
Hanif et.al (2006) described the hypothesis of the Romer’s suggest that inflation is lower in small and
open economies for Pakistan economy. Different studies have been conducted before on inflation and
openness in context of Pakistan but no research work had been done and this is the first such evidence to
model the behavior of inflation by focusing on how more integration with the rest of the world affects
inflation in Pakistan economy. They used the time series data for the period 1973-2005. Inflation is a
complex process and it is very difficult to construct an empirical model for a country but key variables
influencing the inflation in Pakistan can be found so they consider the rate of money growth, real GDP
growth, and growth in Wheat Support Price and over night interest rate as explanatory variables. Firstly,
Augmented Dickey-Fuller (ADF) approach is used to test stationary of the times series used in the
analysis then the model is estimated using HAC estimator and all diagnostic tests are applied on the
equation. When equation passes these tests then a proxy for openness is included. It is tested that if
12
openness has any significant impact on inflation process in Pakistan in the presence of explanatory
variables found significant in the estimated equation. While controlling for all the standard theoretical
determinants of inflation it is concluded that openness has significant negative impact on the domestic
price growth and hence result supports the Romer’s hypothesis.
Vogel (1974) developed a monetary model for explaining inflation in Latin America. The author's model
considered the rate of inflation as a dependent variable and the percentage change in money supply during
current and previous years percentage change in real income during current period and change in inflation
rate lagged by one year and two years as explanatory variables. Vogel concluded that the coefficients of
(Mt and Mt-1) are highly significant and thus indicate that an increase in the rate of growth of money
supply causes a proportionate increase in the rate of inflation within two years. At the same time the rate
of inflation is found to be inversely influenced by the growth rate of real income. The rate of inflation is
not found to be so much influenced by (Pt-1– Pt-2), rather inflation rate lagged by one year, P t-1 has
much influence on the current rate of inflation. The increase in the last equation above is mainly
attributed to the high significance of P t-1.
Schimmelpfennig et.al (2006) examines the factors and assist forecast inflation in Pakistan.
Controlling inflation is a high priority for the policy makers as high inflation obscures the role of relative
price changes and thus inhabits the optimal resource allocation. The simple inflation model is specified
that includes standard monetary variables, the interest rate, the exchange rate, an activity variable and a
wheat support price as the supply-side factor. The monthly data from January 1998 to June 2005 is used
to conclude the results that monetary factor has a dominant role in recent inflation. The basic model used
is estimated both in growth rates and log levels.
Saqib et.al (2009) conduct a study in which investigation of the effects of political instability on
inflation of Pakistan. Most of the studies conducted earlier have not taken into account the simultaneity
that is associated with ordinary least square (OLS) model and hence lead to inconsistent results. The data
13
from 1951-2007 is used to investigate the effects of political instability on inflation of Pakistan. The two
models namely Monetary and Non-Monetary are used. The prediction from Fiscal Theory of Price Level
(FTPL) and PEMP literature is used for investigation. The weak-form FTPL and strong-form FTPL are
used that argued that the price level and hence the inflation is caused by budgetary policies of fiscal
authorities. Two predictions from PEMP literature namely ‘political instability and Deficit Bias’ and war
of attrition are used in the investigation.
Fischer, Sahay and Végh (2002) point out that evaluates the impact of non-monetary inflation
determinants in a world sample comprised by 97 countries over the period 1975-2005. The dependent
variable is the rate of inflation for country in period which is normalized as to avoid giving too much
influence to outlier episodes of high inflation episodes, and independent variables are high inflation
episodes, monetary regimes, structural and institutional variables, cyclical variables, and openness. The
writers point out that there are several reasons to isolate these extreme but infrequent episodes: (a)
hyperinflations are very costly and countries are not willing to tolerate them for more than very few years
or even for only some months. Hence, some hyperinflation episodes may not be accounted for by annual
datasets. (b) Linear estimation models tend to severely over-estimate the impact of inflation on
macroeconomic performance compared estimations using samples of countries where this phenomenon is
absent. The results show the relevance of disciplinary effects in lowering inflation. First, inflation
targeting is estimated to lower inflation when taking as control group all non-inflation targeters for which
there is available information on macroeconomic variables, even after controlling for persistence and
several other determinants of inflation. A different interpretation would conclude that positive supply
shocks that help lower inflation are more prone to be found in developing countries, which is also very
appealing. Finally, we fail to find robust support to the idea that foreign output gap would influence local
inflation for either the short-run or long-run inflation regression models.
Khan and Hussain, 2005 describe a reasonable rate of inflation, around 3 to 6 percent for Pakistan is often
viewed to have positive effects on the economy of pakistan. This study, adopting an econometric
14
framework, focuses on the identification of the main determinants of recent inflation trends. Using data
from the 1972-73 to 2005-06 period, applying ordinary least square method and verifying results through
Breusch-Godfrey Serial Correlation LM and Augmented Dickey-Fuller tests, it finds that the most
important determinants of inflation in 2005-06 were adaptive expectations, private sector credit and rising
import prices. Whereas, the fiscal policy’s contribution to inflation was minimal. The quantitative
analysis reveals that the most significant factors which explain 8 percent inflation in 2005-06 were
inflation expectations, private sector credit (a significant part of asset side of money supply) and imported
inflation. Overall impact of fiscal policies on inflation was not significant and rather the direct part of
taxes was dominant in putting downward pressure on prices. Government sector borrowing also did not
contribute in the rise in prices in 2005-06, though it did contribute in 2004-05. The policy of keeping
stability in the exchange rate was successful in holding the exchange rate from putting further pressure on
prices. The role of wheat support/procurement price and the other unexplained factors were also
insignificant.
15
CHAPTER NO 3
Theoretical Framework
16
Chapter 3
Theoretical FrameworkInflation can be explain and classified on the biases of different factors that cause inflation. It is a very hot and
burning topic in all over the world and present era. Every country wants to control it. Through different
measuring tools we can control inflation. Besides this different studies have been conducted and examine by the
different researchers regarding the inflation targeting strategies and they used different techniques and
methodologies and approaches to explore the impacts of inflation targeting on their economies. Also discussed
the independent variables and find out the relation between two or more variables and further discussed which
variable is mostly effect the inflation economy.
Independent variables:
Money Supply Import Prices
There are different theories of inflation and here we discussed the two main theories that is Demand Pull
Theory and Cost Push Theories. And further discussed the causes of inflation in these theories one by one.
3.1 Demand Pull Theory
Demand Pull inflation occurs due to increase in the demand in the economy is called when consumption
of a particular product are increase the demand also increase if the supply of that particular product is lacking.
So at this stage the demand and supply disequilibrium occurs and due to shortage of these goods people want to
purchase at any prices level. So the result is pries increases and leads to inflation. In demand pull inflation we
discussed two main causes which badly affect the Pakistan economy.
Money Supply
17
Money supply also affects the inflation rate of any economy. Money supply is only occur when the loose
monetary policies of the state bank and its wrong policies. Georgios Karras (2007) said that it’s also shown that
money growth has positive and lasting effects on inflation but possibly will affect output only in the short run or
in the long run. Money is probably neutral.
3.2 Cost Push Inflation
Cost push inflation occurs when the increase in cost of production. Cost of production consist of cost of direct
material, direct labor, and factory overhead and financial administrative and selling expenses also included in it.
So if the cost of production increases the will also increases the prices of product and services to maintain their
profitability. In cost push inflation we discussed two main causes of inflation which are given below.
Import Prices
Devaluation makes our currency cheap in terms of foreign currency. It also makes all those good cheap
which price in terms of rupees and all those goods prices are high which deals in term of dollar or foreign
currency. As a result export price fall and imports price rise. The purchase of imported raw material, goods and
services price will also increase as compare to the domestic cost of production and that is the main cause of
inflation in Pakistan.
18
Import price
Money supply
Inflation
Theoretical Framework
In my study, I am trying to find out the impacts of money supply and import price on the inflation. In this model, money supply and import price is independent variable and inflation is dependent variable. Modern electronic system will have a positive or negative impact on the banking growth.
Dependent Variable
Inflation
Independent Variables
Import Price
Money Supply
19
CHAPTER NO 4
Data & Methodology
20
Chapter 4
Data & MethodologyThe Variables which are used for study are Inflation, Money supply and Import prices.
Inflation (Dependent Variable)
Symbol Used: Inflation (INF)
Money Supply (Independent Variable)
Symbol Used: Money Supply (MS)
Import Price (Independent Variable)
Symbol Used: Import Price (MP)
In the study we want to investigate the correlation and regression/cointegration relationship
between ‘Inflation and Money Supply’ and ‘Inflation and Import Price’ in Pakistan long run as
well in short run. So, in the study the Inflation used a dependent variables and Money Supply
and Import Price as the independent Variables. For the research the annual data will be used on
Inflation, Money Supply and Import Price for the period of 1976 to 2005. As the purpose of the
study is to investigate the correlation relationship between ‘Inflation and Money Supply’ and
‘Inflation and Import Price’ in Pakistan in long run so we collect annual data from 1976 to 2005
form Pakistan for the research. We used the data of 30 years as we want to investigate the
macroeconomics impact of Money Supply and Import Price on Inflation. The data of all above
variables is taken from the international financial statistics (IFS), which is a publication of
21
international monetary fund (IMF), from publications of word bank, annual economics surveys
by the government of Pakistan and the publication of State Bank of Pakistan.
4.1 MethodologyEconomics Model
INFt = βo + β1MSt + β2GGDPt + β3IPt + ε t
A Stationary process
A stationary time series has constant mean. A constant variance and the covariance is
independent of time. Stationary process is essential for standard econometric theory. Without it
we cannot obtain consistent estimators.
First of all it will be checked that whether all above series are stationary or not? To test the
stationary property of all above series, the augmented Dickey-Fuller (ADF) test and Philips-
Perron test will be used. In statistics econometrics, an augmented Dickey-Fuller test (ADF) is a
test for unit root in a time series sample.
Testing Procedure
The testing procedure for the ADF test is
Δ INFt = βo +λ1INFt-1 + β1t +α1∆ INFt-1 + α2 ∆ INFt-2 + …...+ αp ∆ INFt-p + ε t
Where Δ INFt = INFt - INFt-1
Δ MST = βo +λ2MSt-1 + β2t +α1∆ MSt-1 + α2 ∆ MSt-2 + …...+ αp ∆ MSt-p + ε t
22
Where Δ MST = MSt - MSt-1
Δ MPT = βo +λ4MPt-1 + β4t +α1∆ MPt-1 + α2 ∆ MPt-2 +…...+ αp ∆ MPt-p + ε t
Where Δ MPT = MPt - MPt-1
Where βo is a constant, α is the coefficient on a time trend and ρ the lag order of the
autoregressive process. Imposing the constraints α = 0 and βo corresponds to modeling a random
walk and using the constraint βo = 0 corresponds to modeling a random walk with a drift.
By including lags of the order ρ the ADF formulation allows for higher-order autoregressive
processes. This means that the lag length ρ has to determined when applying the test. One
possible approach is to test down from high orders and examine the t-values on coefficients.
The unit root test is then carried out under the null hypothesis γ = 0 against the alternative
hypothesis of γ < 0. Once a value for the test statistic.
DFt =
γSE (γ )
Is computed it can be compared to the relevant critical value for the Dickey-Fuller Test. If the
test statistic is greater (in absolute value) than the critical value, then the null hypothesis of γ =
0 is rejected and no unit root is present.
Following are the hypothesis that will be checked by ADF test.
23
For checking the stationary property in time series of Inflation.
Hypothesis 1
H¿ 0 ¿¿¿: INFt is non-stationary, λ1= 0.
H1 : INFt is stationary, λ1< 0.
Hypothesis 2 for checking the series of Money Supply (MSt )
H¿ 0 ¿¿¿: MSt is non-stationary, λ2= 0.
H1 : MSt is stationary, λ2 < 0.
Hypothesis 3 for checking the series of Import Price (MPt )
H¿ 0 ¿¿¿: MPt is non-stationary, λ4 = 0.
H1 : MPt is stationary, λ4 < 0.
Decision rule:
If p – value > 2.10% value, ==> not reject null hypothesis, i.e. unit root exists.
24
If p – value < 0.10% critical value, ==> reject null hypothesis, i.e. unit root does not exist.
Simple Co - Integression Analysis
When the time series are non- stationary, the simple cointegration test will be used between
Inflation and Money Supply and Inflation and Import Price individually.
And the scatter diagram/ line diagram will be drawn for seeing the relationship between Inflation
and Money Supply and Inflation and Import Price respectively.
Following is the Simple estimation equation for Inflation and Money Supply
INFt = α +β1MSt + ε t
Following is the Simple estimation equation for Inflation and Import Price
INFt = α +β3 MPt + ε t
Correlation:
The correlation is one of the most common and most useful statistics. A correlation is a single
number that describes the degree of relationship between two variables. The correlation test will
be done to check the correlation relationship between Inflation and Money Supply, Inflation and
Import Price.
Test of Goodness of Fit and Correlation:
25
We will test the overall explanatory power of the entire regression; this is accomplished by
calculating the coefficient of determination which is usually denoted byR2. The coefficient of
determination (R2
) is defined as the proportion of the total variation or dispersion in the
dependent variable (about its mean) that is explained by the variation in the independent or
explanatory variables (s) in the regression. In my study the R2
will measure how much of the
variations in the Inflation in Pakistan at long run is explained by the variation in Money Supply
and Import Price respectively, in Pakistan at long run.
R2 =
Explained Variation in Inflation (INFt )Total Variation in Inflation (INF t )
R2 =
Σ ( INF−INF )2
Σ ( INF−INF )2
In the simple regression analysis the square root of the coefficient of determination ( R2) is the
absolute value of the coefficient of correlation, which is denoted by r. That is,
R = √ R2
This is simply a measure of degree of association or co variation that exists between variables
Inflation and Money Supply, and Inflation and Import Price.
Adjusted R 2
Adjusted R2
is a modification of R2
that adjusts for the number of explanatory terms in a
model. UnlikeR2, the adjusted R
2 increases only if the new term improves the model more than
26
would be expected by chance. The adjusted R2
can be negative, and will always be less than or
equal toR2.
It is denoted by R2
R2 = 1 – (1 - R
2)
n−1n−k
Where n is the no. of observations or sample data points and k is the no. of the parameters or
coefficients estimated.
Co-integration test
Cointegration is an econometric property of time series variables. If two or more series are
themselves non-stationary, but a linear combination of them is stationary, then the series are said
to be cointegrated. It is often said that cointegration is a means for correctly testing hypotheses
concerning the relationship between two variables having unit roots.
The two main methods for testing for cointegration are:
1. The Engle-Granger two-step method.
2. The Johansen procedure.
In the study, the Johnson procedure is used to test the cointegration between variables.
Co-integration test for Inflation and Money Supply can be expressed as follows:
INFit = βo +β i MSit + ε t
Where, i = 1, 2, 3, 4, 5, 6………
27
Hypothesis
Ho: There is no co integration is present between Inflation and Money Supply (MS t) at the
significance level in Pakistan, β = 0
H1: There is co integration is present between Inflation and Money Supply (MS t) at the
significance level in Pakistan, β ≠ 0
Now the trace statistics will be used for testing the above hypothesis.
Then if the result is that there is no cointegration present between Inflation and Money Supply,
the Granger Causality test will be used to show the short run relationship between Inflation and
Money Supply, while if the result are, there is cointegration present between Inflation and
Money Supply at significance level then the ECM will be used between Inflation and Money
Supply, so that the result about both short run and long run relationship can be obtained.
Error correction model (ECM)
The ECM will be run with the data at level from as well as at 1st difference form also.
Long run relationship
Hypothesis
Ho= There is no relationship present between Inflation and Money Supply in Pakistan in long run
at the significant level, β = 0
H1= There is relationship present between Inflation and Money Supply in Pakistan in long run at
the significant level, β ≠ 0
28
Now the t-test and F-test will be used for testing the above hypothesis.
Short run relationship
Hypothesis
Ho= There is no relationship present between Inflation and Money Supply in Pakistan in short
run at the significant level, β = 0
H1= There is relationship present between Inflation and Money Supply in Pakistan in short run at
the significant level, β ≠ 0
T-Statistics
After an estimation of a coefficient, the t-statistics for that coefficient is the ratio of the
coefficient to its standard error. That can be tested against a t distribution to determine how
probable it is that the true value of the coefficient is really zero.
The test statistic is a t-score (t) defined by the following equation
t= [(INFt - MSt) – d] / SE, where x1 is the mean of sample1, x2 is the mean of sample 2.
Analysis of Variance
The overall explanatory power of the entire regression can be tested with the analysis of
variance. This uses the value of the F statistics, or F ratio. Specifically, the F statistic is used to
test the hypothesis that the variation in the independent variables explains a significant
proportion of the variation in the dependent variable. Thus, we will use the F statistic to test the
null hypothesis that all the regression coefficients are equal to zero against the alternative
hypothesis that they are not all equal to zero.
29
The value of the F statistic is given by
F =
Explained variation / ( k - 1)Total variation / ( n k )
Where, n is the number of observation and k is the number of regression coefficient. It is because
the F statistics is the ratio of two variances that this test is often referred to as the analysis of
variance. I will calculate the F statistics in terms of the coefficient of determination as follows:
F =
R2 / (k -1 )(1- R2 ) / ( n - k )
Here R2
represent the coefficient of determination between Inflation and Money Supply in
Pakistan in long run.
Then we will compare the calculated value of the F statistics a critical value from the table of the
F distribution. If the calculated value of the F statistics exceeds the critical value of the F
distribution. I will reject the null hypothesis that there is no significant relationship between
Inflation and Money Supply in Pakistan in long run, and I will accept the alternative hypothesis
at 5 % level of significance that not all the coefficient equal to zero, and vice versa.
Co-integration test for Inflation and Import Price can be expressed as follows:
INFit = βo +β i MP + ε t
Where, i = 1, 2, 3, 4, 5, 6………
Hypothesis
30
Ho: There is no co integration is present between Inflation and Import Price at the significance
level in Pakistan, β = 0
H1: There is co integration is present between Inflation and Import Price at the significance level
in Pakistan, β ≠ 0
Now the trace statistics will be used for testing the above hypothesis.
Then if the result is that there is no cointegration present between Inflation and Import Price, the
Granger Causality test will be used to show the short run relationship between Inflation and
Import Price, while if the result are, there is cointegration present between Inflation and Import
Price at significance level then the ECM will be used between Inflation and Import Price, so that
the result about both short run and long run relationship can be obtained.
Error correction model (ECM)
The ECM will be run with the data at level from as well as at 1st difference form also.
Long run relationship
Hypothesis
Ho= There is no relationship present between Inflation and Import Price in Pakistan in long run at
the significant level, β = 0
H1= There is relationship present between Inflation and Import Price in Pakistan in long run at
the significant level, β ≠ 0
Now the t-test and F-test will be used for testing the above hypothesis.
31
Short run relationship
Hypothesis
Ho= There is no relationship present between Inflation and Import Price in Pakistan in short run
at the significant level, β = 0
H1= There is relationship present between Inflation and Import Price in Pakistan in short run at
the significant level, β ≠ 0
T-Statistics
After an estimation of a coefficient, the t-statistics for that coefficient is the ratio of the
coefficient to its standard error. That can be tested against a t distribution to determine how
probable it is that the true value of the coefficient is really zero.
The test statistic is a t-score (t) defined by the following equation
t= [(INFt - Mpt) – d] / SE, where x1 is the mean of sample1, x2 is the mean of sample 2.
F- Test and Analysis of Variance
We will use the F statistic to test the null hypothesis that all the regression coefficients are equal
to zero against the alternative hypothesis that they are not all equal to zero.
The value of the F statistic is given by
F =
Explained variation / ( k - 1)Total variation / ( n k )
32
Where, n is the number of observation and k is the number of regression coefficient. It is because
the F statistics is the ratio of two variances that this test is often referred to as the analysis of
variance. I will calculate the F statistics in terms of the coefficient of determination as follows:
F =
R2 / (k -1 )(1- R2 ) / ( n - k )
Here R2
represent the coefficient of determination between Inflation and Import Price in
Pakistan in long run.
Then we will compare the calculated value of the F statistics a critical value from the table of the
F distribution. If the calculated value of the F statistics exceeds the critical value of the F
distribution. I will reject the null hypothesis that there is no significant relationship between
Inflation and Import Price in Pakistan in long run, and I will accept the alternative hypothesis at
5 % level of significance that not all the coefficient equal to zero, and vice versa.
Multi cointegration Analysis
The Multi cointegration Model
The multi cointegration model will be used for testing the cointegration between Inflation,
Money supply and Import Price.
INFt = βo + β1MSt + β2IPt + ε t
I is dependent variable while MS and IP are the independent variables. Whileε t is the error
affect the Inflation in Pakistan while we will just check that how much changes in Money Supply
and Import Price.
33
Then we will calculate the values of βo, β1, β2, and then we will calculate the value of R2
and
adjusted R2
and at last we will use F statistics to test the hypothesis that the variation in the
Money Supply and Import Price explains a significant proportion of the variation in the Inflation.
Cointegration Test
Hypothesis
Ho: There is no co integration is present between Inflation, Money Supply and Import Price at the
significance level in Pakistan, β = 0
H1: There is co integration is present between Inflation, , Money Supply and Import Price at the
significance level in Pakistan, β ≠ 0
Now the trace statistics will be used for testing the above hypothesis.
Error Correction Model (ECM)
The ECM model will be used for the finding the effects of changes in Money Supply and Import
Price on Inflation in Pakistan and to test the whether Money Supply and Import Price explains
significant variation of Inflation or nor? Is yes, then how much?
Hypothesis
34
Ho: The Money Supply and Import Price combine do not explain a significance proportion of the
variation in Inflation in Pakistan in long run, β = 0.
H1: The Money Supply and Import Price combine explain a significance proportion of the
variation in Inflation in Pakistan in long run, β ≠ 0.
We will test above hypothesis by using F statistics.
F =
R2 / (k -1 )(1- R2) / ( n - k )
Here R2
represent the coefficient of determination between Inflation, Money Supply, and
Import Price in Pakistan in long run.
Then we will compare the calculated value of the F statistics a critical value from the table of the
F distribution. If the calculated value of the F statistics exceeds the critical value of the F
distribution. I will reject the null hypothesis that there is no significant relationship between
Inflation, Money Supply and Import Price in Pakistan in long run, and I will accept the
alternative hypothesis at 5 % level of significance that not all the coefficient equal to zero, and
vice versa.
35
CHAPTER NO 5
Data Analysis
36
Chapter 5
Data Analysis
Unit root test:
Use the test “Augmented Dickey Fuller” and see that the data is stationary or non-stationary on
the level, 1st difference and 2nd difference. All the data are stationary on the 2nd difference.
Inflation
Augmented- Dickey Fuller Unit Root Test on D (INF, 2)
Null Hypothesis: D(INF,2) has a unit root
Exogenous: Constant
Lag Length: 4 (Automatic based on SIC, MAXLAG=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -5.088395 0.0005
Test critical values: 1% level -3.752946
5% level -2.998064
10% level -2.638752
*MacKinnon (1996) one-sided p-values.
37
First of all, to check the stationary property of the Inflation study we apply Augmented Dickey
Fuller test at level, 1st difference and 2nd difference therefore the series of Inflation is stationary
on 2nd difference at 1% significance level because the Augmented Dickey-Fuller Test statistic
value (5.088395) is greater than the critical value at 1% level (3.752946). As the P value is less
than 0.05 so we reject the null hypothesis in favor of alternative hypothesis which show this
series is stationary and there is no unit root.
Money Supply
Augmented- Dickey Fuller Unit Root Test on D (MS, 2)
Null Hypothesis: D(MS,2) has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic based on SIC, MAXLAG=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -4.478232 0.0015
Test critical values: 1% level -3.699871
5% level -2.976263
10% level -2.627420
*MacKinnon (1996) one-sided p-values.
First of all, to check the stationary property of the Money Supply on study we apply Augmented
Dickey Fuller test at level, 1st difference and 2nd difference therefore the series of Money Supply 38
is stationary on 2nd difference at 1% significance level because the Augmented Dickey-Fuller
Test statistic value (4.478232) is greater than the critical value at 1% level (3.699871). As the P
value is less than 0.05 so we reject the null hypothesis in favor of alternative hypothesis which
show this series is stationary and there is no unit root.
Import Price
Augmented- Dickey Fuller Unit Root Test on D (MP, 2)
Null Hypothesis: D(MP,2) has a unit root
Exogenous: Constant
Lag Length: 4 (Automatic based on SIC, MAXLAG=7)
t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -6.664508 0.0000
Test critical values: 1% level -3.752946
5% level -2.998064
10% level -2.638752
*MacKinnon (1996) one-sided p-values.
First of all, to check the stationary property of the Import Price on study we apply Augmented
Dickey Fuller test at level, 1st difference and 2nd difference therefore the series of Import Price is
stationary on 2nd difference at 1% significance level because the Augmented Dickey-Fuller Test
statistic value (6.664508) is greater than the critical value at 1% level (3.752946). As the P value 39
is less than 0.05 so we reject the null hypothesis in favor of alternative hypothesis which show
this series is stationary and there is no unit root.
Johansen Co-integration Test:
Co-integration:
Co integration is an econometric property of time series variables. If two or more series are
themselves non stationary, but a linear combination of them is stationary, then the series are said
to be co integrated. It is often said that co integration is a mean for correctly testing hypothesis
concerning the relationship between two variables having unit roots. If the results show that there
is no co-integration between the variables then Granger Causality Test will be used to show the
short run relationship between the variables. But, if there is no co-integration between the
variables the there will be used Error Correction Model to show the long run relationship
between the variables.
40
Co integration test for Money Supply, Import Price and Inflation:
Date: 12/18/09 Time: 23:41
Sample (adjusted): 1978 2005
Included observations: 28 after adjustments
Trend assumption: Linear deterministic trend
Series: MS INF MP
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized No. of CE(s) Eigen value
TraceStatistic
0.05Critical Value Prob.**
None * 0.731322 56.50835 29.79707 0.0000At most 1 * 0.412374 19.70958 15.49471 0.0109At most 2 * 0.158231 4.822984 3.841466 0.0281
Trace test indicates 3 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
41
INFt = βo + β1MSt + β2IPt + ε t
Where, i=1, 2, 3, 4, 5…..
Ho: There is no co integration is present between Inflation, Money Supply and Import Price at the
significance level in Pakistan, β = 0
Versus
H1: There is co integration is present between Inflation, , Money Supply and Import Price at the
significance level in Pakistan, β ≠ 0
Now the trace statistics will be used for testing the above hypothesis.
Co-integration test represents the long run relationship among the variables. The results show
that there is a long run relationship between the Inflation Money Supply and Import Price. The
results also show that the trace value is greater than the critical value which shows that there is a
long run relationship in these variables. The co-integration test indicates that there are three co-
integration equations and reject the null hypothesis and accept alternative hypothesis at the 0.05
level of significance as shown. Trace value indicates 3 equations at the 0.05 level.
42
Error Correction Model: (ECM)
Dependent Variable: D(INF)
Method: Least Squares
Date: 12/18/09 Time: 23:43
Sample (adjusted): 1978 2005
Included observations: 28 after adjustments
Variable Coefficient Std. Error t-Statistic Prob. C 0.885727 3.324317 2.643926 0.0267
LgD(MP) 0.606741 0.307312 1.974349 0.0419LgD(MS) 2.67E-11 2.30E-11 2.170306 0.0322LgMP(-2) -0.086942 0.244181 2.740128 0.0174LgMS(-2) -2.92E-12 1.45E-11 2.422857 0.0235LgINF(-2) -0.088661 0.302572 -2.07929 0.0337
R-Squared 0.817432Adjusted R-squared 0.796514Durbain-Watson 2.190545F-statistic 24.46032Prob(F-statistic) 0.001407
The ECM will be run with the data at level form as well at 2nd difference form.
Hypothesis
43
Ho= There is no relationship present between Inflation, Money Supply and Import Price in
Pakistan in long run at the significant level, β = 0
Versus
H1= There is relationship present between Inflation, Money Supply and Import Price in Pakistan
in long run at the significant level, β ≠ 0
Now the t-test and F-test will be used for testing the above hypothesis.
When the cointegration test identify that there is a long run relationship among the variables,
we will test the ECM rather than the Granger Causality test. ECM is basically used to
investigate the long run and short run relationship between the variables. By applying it we
find the following results.
In these results, C = 0.885727 shows constant coefficient β1 and β2 are the partial coefficients
respectively. The value of β1 is 0.606741 shows that one unit change in Import Price brings
0.606741 units change in dependent variable Inflation and there is a positive relationship
between the Inflation and Import Price in long run and reflects the null hypothesis at 0.0419
probability. While the value of β2 is 2.67E-11 shows that one unit change in Money Supply
brings 2.67E-11 units change in dependent variable Inflation and there is a positive
relationship between the Inflation and Money Supply in long run and reflects the null
hypothesis at 0.0322 probability.
R-squared or overall goodness:
44
It measures the proportion of variability in the values of the dependent variable explained by
its linear relation with the independent variables. In my study it will measure how much of
the variation in Inflation in Pakistan at long run is explained by the variation Import Price
and Money Supply. It is computed by
R2 =
Explained Variation in Inflation (INFt )Total Variation in Inflation (INF t )
The value of it in my study is 0.817432 represents that the variation in dependent variable
Inflation are 81% explained by the independent variables Import Price and Money Supply in
Pakistan.
Adjusted R-squared:
Adjusted R2
is a modification of R2
that adjusts for the number of explanatory terms in a
model. UnlikeR2, the adjusted R
2 increases only if the new term improves the model more
than would be expected by chance. The adjusted R2
can be negative, and will always be less
than or equal toR2.
It is denoted by R2
R2 = 1 – (1 - R
2)
n−1n−k
45
The value of it in my study is 0.796514 represents that the variation in dependent variable
Inflation is 79% explained by the independent variables Import Price and Money Supply in
Pakistan.
F-Statistics:
It is computed by
F = R2/(k−1)
(1−R2)(n−k )
If its value is greater than its probability then we reject the H0 and accept the H1. In my study
F-statistics value 24.46032 is greater than its probability 0.001407. So, it indicates that
independent variables Import Price and Money Supply jointly affect the dependent variable
Inflation. At the end, the value of Durbin Watson test is 2.19 shows positive auto correlation.
After these result, we say that our model is good fit.
46
CHAPTER NO 6
CONCLUSION
47
Chapter 6
Summary
The present study has been made to understand the relationship between import price, money
supply and inflation in Pakistan by applying different techniques and produced different results .
To check the stationary of the dependent and independent variables applied the unit root test.
This test shows that the data is stationary at 2nd difference on (ADF).
To check the relationship between the variables, cointegration test is applied altogether on all the
variables. The prob. Value which is less than 0.05 shows that import price, money supply and
inflation are co integrated with each other, which indicates that the result is good.
At the end to check the long run relationship between the variables, error correction model is
applied. The results of ECM shows that all variables have long run relationship with each
other. Because the value of t-statistic is more than 2, the prob. value of ECM is also less than
0.05, the value of R-squared is greater than adjusted R-squared, the value of Durban-Watson
48
stat is also more than 2 and the prob. of f-statistic is 0.001407 which shows that the model is
good and relationship between all variable is very strong.
CONCLUSION
In this paper an effort has been made to understand the relationship between import price, money
supply and inflation in Pakistan. This study has investigated the determinants of inflation for the
period 1976-2005 in the case of Pakistan. This paper employs the co integration analysis and
error correction model to investigate the effect of import price and money supply on inflation.
The Inflation has been used as dependent variable while Import Price and Money Supply as
independent variables were used to investigate the impact of inflation created by such
independent variables in the economy of Pakistan. The overall conclusion is that we find
significant relationship between dependent variable (inflation) and independent variables
( Import Price and Money Supply). The overall conclusion is that import price and money supply
has a long-run positive impact on inflation in Pakistan.
In this study there are some limitations. One limitation of this study is the use of only import
price and money supply data. While others variables also affect the inflation, like interest rate,
49
technical change and human capital growth etc and others macro economic variables under
devolutionary policies. In this situation, the direction of the inflation is undetermined. Future
research should try to use mutual available to investigate it in Pakistan. Researchers interested in
extending this study should try to investigate the response of other variables in the model. This
will help understand the effect of import price and money supply on inflation on the economy as
a whole.
CHAPTER NO 7
References
50
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