umair mazher thesis

8/22/2019 Umair Mazher Thesis

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COMMODITY PRICE MOVEMENTS AND PCE INFLATION

A thesis submitted

By

Umair Mazher (3371)

To

Dr. Muhammad Azam

Department of Business Administration

In partial fulfillment of

The requirement for the

Degree of

MASTER OF BUSINESS ADMINISTRATION

In

FINANCE

This thesis has been

Accepted by the faculty

FACULTY OF BUSINESS ADMINISTRATION

By:

_________________________________

Dr. Muhammad Azam


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i

Acknowledgement

Dear Reader,

I would like to give my humble thanks to Dr. Muhammad Azam, my respected teacher and

supervisor in the final MBA thesis for guiding and facilitating me from the beginning to the

completion of my topic on Commodity Price Movements and PCE Inflation. Apart from that

also giving me the chance to implement the knowledge and skills I have gained during the

different courses of MBA program. This was a wonderful experience on my part as it helps me to

enhance and compile my core knowledge and skills.

I would also like to thank Mr Ali Raza, Mr Tehseen Javaid and Mr Farhan Mehboob for sharing

with me their vast and valuable amount of knowledge that helped me a lot in completing this

report successfully.

Regards,

Umair Mazher

(MBA-3371)


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ii

Table of contents

Acknowledgement .......................................................................................................................... i

Abstract ......................................................................................................................................... iv

Chapter one ....................................................................................................................................1

1.1 Introduction ............................................................................................................................2

1.2 Background ............................................................................................................................3

1.3 Statement of problem .............................................................................................................4

1.4 Research question ..................................................................................................................4

1.5 Significance of the study ........................................................................................................5

1.6 Limitation of the study ...........................................................................................................5

Chapter two ....................................................................................................................................6

Literature review ...................................................................................................................7

Chapter three ...............................................................................................................................17

3.1 Methodology ........................................................................................................................18

3.2 Quantitative research approach ............................................................................................18

3.3 Correlational design .............................................................................................................18

3.4 Data ......................................................................................................................................19

3.5 Statistical technique ..............................................................................................................19

3.6 Hypothesis ............................................................................................................................19

3.7 Model ...................................................................................................................................19

3.7.1 Personal consumption expenditure (PCE) ..............................................................20

3.7.2 Oil price ..................................................................................................................21


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iii

3.7.3 Major crop price .....................................................................................................21

3.7.4 Minor crop price .....................................................................................................21

Chapter four .................................................................................................................................22

4.1 Descriptive statistics table ..................................................................................................23

4.2 Correlation coefficients table .............................................................................................24

4.3 Multicollinearity diagnostics table .....................................................................................25

4.4 Result Summary table ........................................................................................................26

Chapter five ..................................................................................................................................28

5.1 Findings..............................................................................................................................29

5.2 Recommendation ...............................................................................................................29

5.2 Future Recommendation ....................................................................................................30

References .....................................................................................................................................31

Appendix .......................................................................................................................................35


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iv

Abstract

This paper investigates the relationship between movement in commodity prices and personal

consumption expenditure (PCE) inflation in the Pakistan economy. The methodology used in the

study for the purpose of identifying the impact of changes in oil and crop prices on the level of

PCE inflation in Pakistan economy, is multiple regression analysis. The annual data for the past

50 years is used in the study for the period 1961 to 2010. The findings of the study provide

evidence that there is a significant impact of oil price and major crop prices on PCE inflation in

Pakistan. On the other hand, minor crop price was found to have a moderate impact on the level

of PCE inflation in the Pakistan economy.


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Commodity Price Movements and PCE Inflation 1

Chapter 1

Introduction


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1.1 Introduction

Economy of Pakistan is facing a number of problems including bad governance, unfavorable

balance of payments, political instability, ongoing global recession and last but not the least

inflation. Generally inflation can be best determined as a regressive type of taxation for the poor.

Pakistan successfully restored the macroeconomic activity as a result of balance of payment

crises occurred in 2008. However it is absolutely challenging for Pakistan to consolidate such an

early gain with a substantial amount of inflation (especially regarding private expenditure)

emerging in the economy.

According to many researches and studies Adnan Hussain and Sadaf Majeed (2010) Pakistan is

found to be a consumption oriented country, so personal consumption expenditure in Pakistan

has a huge impact on the countrys GDP. In Pakistan more than 24% (according to CIA world

fact book estimate 2011) of the population is living below the poverty line, and for this

percentage of population the great deal of interest is with the (personal consumption expenditure)

PCE inflation1.

The recent flood disaster in different areas of Pakistan especially rural areas (that accounts for

most of the agricultural production) has badly affected the agricultural concerns and feasibility.

So fluctuation in the prices of different agricultural products of Pakistan also implies some

effects on the general price level as well as on the prices of personal consumption goods and

services. Apart from that taking in view the increasing population rate of Pakistan we can predict

an increased level of private consumption in the Pakistan economy with the passage of time. So

continuous increase in the prices of some of the main crops of Pakistan like cotton, wheat, rice

etc are most likely to increase the personal consumption expenditure with the evidence from the

past.

1 The PCE price index is produced by the Bureau of Economic Analysis (BEA) and measures the prices of goodsand services purchased by persons, individuals, and nonprofit institutions in the National Income and ProductAccountsso-called personal consumption expenditures (PCE).


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Similar is the case for oil, as Pakistan is not a leading oil producer in the world and its total oil

production is a way less than oil imports in Pakistan. As the oil production in Pakistan according

to CIA world fact book is around 68,220 barrels per day (bbl/day) as at year 2011, and the oil

exports in the same year are estimated at 23,230 (bbl/day). But the oil import in Pakistan in the

present year is estimated at around 278,900 (bbl/day). So this means Pakistan is consuming oil a

lot more than it produces, this clarifies the intensive level of demand for oil in Pakistan.

Fluctuations in the international oil prices in the few recent years provide a strong evidence of

increasing nominal level of personal consumption expenditure in the country.

1.2 Background

Pakistan as an under developed country had always been dependent on many other countries to

fulfill its consumption needs in the form of imports. On the other hand economic stability and

income inequalities also have contributed a lot towards changing consumption patterns in

Pakistan. In this respect major commodity price fluctuations also have a significant impact on the

personal consumption expenditures of people in Pakistan.

Major commodities like oil and crops in Pakistan are to have a direct relationship with

consumption pattern of people. This ultimately provides a relationship between oil prices and

PCE inflation. The reason for such a relationship is that oil is one of the major inputs in an

economy. Its usage in the economy includes critical activities like heating homes, fueling

transportation etc. So increase in oil prices will in turn increase the cost of inputs and so the cost

of output, resulting in increased level of core inflation in the economy.

Similarly agricultural products of a country account for a large proportion in the total

consumption of a country. As in a country, (especially agricultural country) agricultural products

amount to be major inputs. E.g. increases in the price of sugar cane in a country will increase theprices of inputs to the sugar factories, and will ultimately increases the price of sugar available

for consumption and other purposes. Apart from that we can also take the example of tobacco

price increases causes increase in the price of cigarettes in the country. So there is also a direct

relationship between the agricultural prices and the level of inflation in a country. And an


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increase in the prices of agricultural products will also in turn increase the core inflation rate in

the country.

1.3 Statement of problem

Over the past seven years since 2003 PCE inflation rate in Pakistan is continuously rising

(according to handbook of statistics on Pakistan economy). With reference to a previous research

done in this regard concerning the US PCE index by (Bart Hobijin 2008), measures the consumer

price increases with respect to increase in commodity prices.

But this US inflation research is just estimating the input output share of commodity products for

the period of ten years (1998 2008) which may not provide the fuller picture of the trend of

commodity price movements with PCE.

While we in our research are working with a comparatively larger sample of data for the period

of 50 years from 1961 to 2010 in order to dig out the relationship between commodity prices and

PCE.

1.4 Research question

Do oil and crop prices have a significant impact on the level of PCE inflation in Pakistan?


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1.5 Significance of the study:-

This study addresses one of the major reasons of macroeconomic problems prevailing in the

economy of Pakistan. That is the increasing level of PCE inflation and decreasing purchasing

power of people. Although level of economic growth2

in an economy can be raised through

increasing the national income with an increase in PCE inflation but for the purpose of

increasing the level of economic development3 it is very important to improve the living

standards of people living in the economy. And it can be only achieved by controlling the level

of PCE inflation so to eliminate the inequalities in the income level of every individual. In this

regard this study will strongly contribute to serve such economic prospects.

1.6 Limitations of the study:-

Limitations of this study include certain assumptions that we will take in view for the purpose of

our analysis such as rate of increase in PCE inflation is same as the rate of decrease in the

purchasing power of individuals and households. As in practice both rates may not be equal due

to consumers attention towards substitute products that is why here we use the word

approximately equal. Apart from that the topic just has one exactly same research on US

inflation by (Bart Hobijin 2008) and a limited number of other relevant researches.

2 Economic growth is an increase (or decrease) in the value of goods and services that a geographic area produces

and sells compared to an earlier time.

3 Economic development is a broad term that generally refers to the sustained, concerted effort of policymakers

and community to promote the standard of living and economic health in a specific area.
http://en.wikipedia.org/wiki/Policymakershttp://en.wikipedia.org/wiki/Communityhttp://en.wikipedia.org/wiki/Standard_of_livinghttp://en.wikipedia.org/wiki/Economic_expansionhttp://en.wikipedia.org/wiki/Economic_expansionhttp://en.wikipedia.org/wiki/Standard_of_livinghttp://en.wikipedia.org/wiki/Communityhttp://en.wikipedia.org/wiki/Policymakers


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Chapter 2

Literature review


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Literature review:-

Hobijin (2008) investigates the effects of commodity price movements on the US inflation by

taking in view the run up in energy and crop prices. For this purpose the sample data taken is the

crop and grain prices for period 1998-2008 and energy prices for the similar period. To identify

the contribution of commodity prices to the US inflation for the personal consumption

expenditure an input-output table is used that measures a commoditys fraction of dollar of

output that will attribute towards the use of various commodities as inputs. Findings of the study

include the evidence about a significant increase in the consumer prices due to increases in the

commodity prices.

Odior & Banuso (2011) conducted a research for exploring the effect of household welfare of

macroeconomic volatility in Nigeria on the (PCE) Private consumption expenditure. The

methodology used in the study is a dynamic macro econometric stochastic model| for the

purpose of analyzing the impacts, where PCE presumes to be dependent upon various changes in

macroeconomic performance indicators. The (SVAR) structural auto regression process that

includes the general price level, PCE, unemployment rate, real exchange rate and debt service

ratio is estimated for a period of 28 years from 1980-2008. The findings of the study state that an

economic shock on the level of inflation will have a strong effect on the level of PCE in case of a

long run horizon.

\Kelly (2011) Conducted a recent study on the US inflation pressures, the purpose was to identify

the relationship between the commodity prices and the personal consumption expenditure and to

predict the level of inflation and PCE price index in upcoming years.. Data sample is taken for

the period from 2000 to 2011. The methodology used in the study is the method of least squaresto find out the relationship of PCE index with the fluctuations in the volatile commodities. The

findings of the study allow us to predict that the inflation level (PCE index measurement) as well

as the core inflation in US will rise above 2% in September 2011 quarter.


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Hakro & Omezzine (2010) investigates the long run and short run relationships of global oil

price and external food shocks in the economy of Oman. The (VAR) vector autoregressive

model with its forecast error variance decompositions, impulse response functions and error

correction model methodologies are used for the purpose of tracing out the impact that external

shocks have on the domestic economy. Findings of the study reveal that oil price shocks

significantly affect the real output in case of long run. While the food price shocks have a

negative effect on the real output as it causes an increase in the level of commodity prices.

Abbas (2009) investigates the impact of inflation inertia, external and monetary oil price

changes, real GDP growth and crop productivity propositions on the level of inflation in

Pakistan. The data used in the study is for the period from 1981 to 2007. The empirical findings

of the study include that in Pakistan food price inflation also works as a monetary

phenomenon. While on the other hand continuous persistence does hold in inflation due to the

fact that autocorrelation is absent in money supply. Evidence is also found that external and

monetary changes in the oil price have a significant impact on the level of inflation in the

Pakistan economy.

Bullard (2011) describes the effects of commodity prices on US inflation and also evaluates the

core inflation versus the headline inflation to answer the question that which one is to be

observed for the purpose of preparing US monetary policy. The data is taken of the energy price

index, food index, agriculture index for the period 2005 to 2011 and for oil and gas prices for the

period 1991 to 2011. The conclusion of the study reports that headline inflation is of more

importance than core inflation when it comes to monetary policy goals. Similarly of the older

(CMS) commodity money standards, targeting inflation is the modern equivalent.


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Evans (2011) conducted a research for finding out that what are the policies and implications

available to the US government in terms of commodity prices for monetary policy and inflation.

This study uses the method of evaluating different future indicators of inflation to assess the

inflationary pressures. Three hypotheses are used in the study weak credibility hypothesis of the

central bank, strong credibility hypothesis of the central bank and a general indicator hypothesis

that is uninformative. The findings states that an increase in the oil prices of 10% and in the

(commodity research bureau) CRB commodity prices of 3% will ultimately contribute towards

an unanticipated rise in the level of PCE index or in other words in the level of core inflation.

Edelstein (2007) identified the importance of considering the commodity prices in forecasting

the level of inflation in US and also its contributions in monetary policy making. For this

purpose different forecasting methods are used to estimate their credibility such as Bayesian

shrinkage estimation, bagging forecasts, factor models, equal-weighted forecasts etc. Sample for

data was taken for the period of 1993 to 2004. It was found that each of these methods is not

effective to forecast the US consumer price index (CPI) when compared with the inflation-only

model. This study also identifies that looking at commodity prices will increase the effectiveness

of Taylor rule in predicting and forecasting the inflation level. However inflation forecasting will

only contribute up to a little extent in monetary policy making.

Jamali et. al(2010) investigates that what is the relationship between the macro economy and the

changes in crude oil prices in Pakistan. The methodology used in the study is the multivariate

(variance autoregressive) VAR analysis. The macro economic variables used in the study are

long term interest rates, short term interest rate, money supply, real effective exchange rates and

real GDP. The model reveals a significant and negative impact of oil price changes on the output

level in Pakistan. There is also clear evidence that oil price changes also have a significant

impact on short term interest rates, real exchange rates and money supply. The study concludes a

negative impact of oil price changes on the Pakistan economy


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Wang (2010) examined the relationship between a real economic activity and oil prices in three

countries (Japan, China and Russia) by using a variance decomposition analysis and a (VAR)

model for the period 1999-2008. Data sample taken is of the monthly observations of the oil

price index and the real economic variables. Results of this study shows a long run relationship

among oil prices and economic activity, as both the variables during the sample period were

moving together in a similar way. Secondly, the variance decomposition analysis results show

that in Russia there is a significant impact of oil price shocks on the real economic activity.

Hussain et. al(2006) Conducted a research for the purpose of predicting the level of demand for

money in Pakistan, through a number of determinants such increasing inflation level, monetary

policies and especially food inflation. This study uses a 33 year time series data (period from

1972-2005) for the purpose of money demand estimation in Pakistan. This data is taken from

SBP (State Bank of Pakistan). The methodology used in the study is the unit root and co-

integration test. Findings of the study show no unit root or co-integration.

Reicher & Utlaut (2011) conducted the study to investigate that what are the inflation effects on

the commodity prices in the United States. The quarterly data used in the study is taken from the

period 1970 to 2010. The method used in the study is VAR model for studying the impact of

inflation on commodity prices. Findings of the study show that the inflation expectation in the

long run has a large middle effect on both the short run inflation expectation and commodity

prices.


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Chou & Tseng (2011) examined the short and long term effects of oil price fluctuations on the

level of inflation in Taiwan. He employs the core index, various sub indices and CPI index for

evaluation by using ARDL model and combining it with an augmented Philips curve. The data

sample used in this study is the monthly figures for the period 1982-2010. The findings of the

study shows a significant impact of relative oil prices on the CPI of Taiwan in the long run while

in the short run the impact is not significant.

Nosheen et. al(2010) conducted a research for the purpose of presentation of data for 8 countries

on economic performance (inflation, external account, aggregate growth, sectoral growth and

investment). The data used in the study is taken on annual basis with respect to the countrys

policy in that particular year as weak, moderate or strong reformers. The findings of the study

surprisingly show a greater level of growth in manufacturing if we take in view the weak

reforms.

Morgan (2011) identified that US core inflation is pushed by the fluctuations in the import

prices. He had taken the data for the period from 2005 to 2011 of US import prices, its

contribution to personal consumption expenditure (PCE), energy prices and oil prices etc and

uses a series of rolling regressions model. He also identified the increasing level of inflation in

China causing the increase in the price of goods exported to US. This will ultimately increase the

level of core inflation in US. The findings of the study shows that quarterly core inflation in US

will respond with a 0.56% increase towards a 10% increase in the non-oil import prices in the

same quarter. While in the next quarter the effect will be followed by a 0.02% increase in the

core inflation in US.


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Ghalayini (2011) investigates the volatility in the oil prices in a country as a determinant of

economic growth. He also wants to investigate that what are the differences in the effects of oil

price change on economic growth among different group of countries. This paper uses the data

that includes G-7 group of countries in addition to China, India and Russia. The methodology

used in the study is the Granger causality test. The major findings of the study includes the

evidence that interaction between the economic growth and oil price volatility is not proven in

case of most countries in our analysis, while for the G-7 group of countries there lies a

unidirectional relationship between oil price and GDP.

Syed (2010) investigates the impact of inflation, changing oil prices, domestic investment, FDI

and consumption on the (GDP) Gross Domestic Product of Pakistan. The study uses 30 years

secondary data for the period from 1979 to 2009 taken from SBP, Economic survey of Pakistan

economy. Stationarity of data is checked through (ADF) Augmented Dicky Fuller unit root test.

The method of ordinary least squares is used to find out the relationship between the independent

and dependent variables. The major findings of the study show a negative relationship between

oil price volatility, inflation and GDP.

Abdullah & Kalim (2009) investigated the main determinants of food price inflation. For the

purpose of this study they use the ADF unit root test and the co-integration test for determining

the stationarity and non stationarity of data. Data sample is taken of concerned variables for the

period 1972-2008. Findings of the study indicates that rise in the food price inflation had caused

significant increases in the living cost of individuals and households which results in

productivity losses. Another thing they identified is that poor people are more affected by this

rise in food prices because they need to spend more than half of their incomes on food.


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Mohammad (2010) conducted an exploratory research in Pakistan for the purpose of analyzing

the correlation between export earning and oil price volatility. The data used in the study is the

annual data of 33 years from the period 1975 to 2008. This data is been taken from International

Financial statistics (IFS) and World development indicator (WDI). The methodology used for

data analysis is Augmented Dicky Fuller (ADF) unit root test. Empirical findings of the study

conclude that there lies a negative correlation between oil price and export earning in Pakistan

which may have its adverse effects on the economy.

Helmy (2010) conducted a research in Egypt for the purpose of analyzing the inflation dynamics

during the past 30 years by taking in view the importance of various inflation sources. Another

purpose of this paper is to investigate that what is the impact of trade deficit in Egypt on the level

of inflation. The paper uses a (VDC) variance error decomposition, (VAR) variance

autoregressive model, (IRF) impulse response functions and Granger causality test for analyzing

the inflation dynamics and sources in Egypt. Results of the study reveal that rate of growth of

money supply primarily affect the level of inflation in Egypt. There is also enough evidence to

refer the interest rates as secondary determinant of inflation.

Trung & Vinh (2011) conducted a study to examine that what is the impact oil prices have on the

level of economic activity in Vietnam. For this purpose the methodologies used in the study are

the co integration techniques and (VAR) vector autoregressive modeling. The data used in the

study is monthly data of 14 years which from 1995 to 2009. Real effective exchange rate and

inflation are also used as economic activitys additional determinants. The findings of the study

include a strong evidence of a long term relationship between exchange rate, economic activity,

inflation and oil prices. Hence oil price increases will result in an enhancement of economic

activity in Vietnam and there is a positive impact of inflation on economic activity.


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Rabbani et. al(2009) developed a study by constructing a dynamic model for the price of wheat

in Bangladesh. The data used in the study is the quarterly data for wheat wholesale price from

1984 (quarter 3) to 2008 (quarter 4). The methodology used in the study is a single equation

(ARIMA) Autoregressive integrated moving average model of the quarterly wholesale price of

wheat. The model concludes the 12 future forecasts for the purpose of usage in policy instrument

for importers, sellers and producers.

Huang & Tseng (2010) investigated the interaction between two key macroeconomic variables

the exchange rate and the crude oil prices in United States of America. The auxiliary regression

approach is used in the study in two steps for the purpose of identifying the effect of exchange

rates on the price of crude oil and also the affect on the equilibrium price of oil. The data used in

the study is taken from three crude oil prices and the effective US exchange rate index for the

period of twenty years. Findings of the study reveal a two way significant and causal relationship

between exchange rate and oil price dynamics.

Manzooret.al(2011) conducted a research for the purpose of identifying the impact of inflation

on the household consumption in Pakistan. The methodology used in the study is analysis of

variance (ANNOVA) for the purpose of testing the hypothesis. Selection was made of about 400

respondents as the initial data sample but 95 questionnaires were either incomplete or not

received so a sample of 305 respondents was used. The findings of the study gives a strong

evidence that an increase in the inflation level will decrease the household consumption due to

decrease in the value of money.


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Hussain et.al(2006) examined the competitiveness of sugar cane production and its economics

in the upcoming trade economy in Pakistan. The study also analysis the extent of agricultural

safeguard and policy bend. Sindh and Punjab the major producers of sugar cane were focused in

the study. Collection of production cost data of sugar cane was from (APCom) agricultural prices

commission. The data was for the period 1990- 2002. Findings of the study lead us to a

conclusion that production of sugar cane has no comparative advantage whatsoever at the export

parity price.

Hsing (2007) conducted a research to test the impact of different macroeconomic variables and

real crude oil prices on the level of output in the German economy. For this purpose the

methodology used in the study is linear regression. The conclusion of the study provides the

evidence that real crude oil prices may have an impact on the German output or may not. It

depends on whether the critical value is less or greater than $46.29 per barrel. In addition, a

lower real world interest rate, a lower government consumption spending ratio to GDP, a lower

inflation, a higher real stock price would cause an increase in the level of real output.

Papapetrou (2009) conducted a research for the purpose of studying the relationship between the

level of economic activity and the oil price fluctuations in Greece. The data used in the study is

for the period 1982 to 2008. The methodology used by the researcher is (TA-R) threshold

regression model and a (RS-M) switching regime model for estimating the relationship. The

study concludes that as far as (RS-M) industrial production which is a strong indicator of

economic activity will only be significantly and negatively affected by oil price increases if they

increase more than 3% per month. Similarly (TA-R) model suggests that industrial production

will only be significantly and negatively affected by oil price increases if they increase more than

3.37% per month.


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Jin (2008) empirically analyzes the effects of real exchange rate and oil price on the level of real

economic activity in Japan, China and Russia. The data used in the study is the international

crude oil prices, the (REER) real effective exchange rate of Yen, Yuan and Ruble and the real

GDP of Japan, China and Russia from the period 1999 to 2007. The methodology of the study

includes the Granger- Causality testing with the help of a LA-VAR technique. Grangers

causality test indicates that international oil price causes an increase in all countries GDP and the

exchange rate causes an increase in the Japanese and Russian GDP. While the long run test

findings suggest that there is a significant contribution of high oil prices in the economic growth

of Russia, in case of Japan in the long run both variables lower the GDP and in China there was

no co integration found in the long run.

Aliyu (2009) conducted a research to investigate the effect of real exchange rate fluctuation and

changing oil prices on the level of economic growth in Nigeria. The data used in the study is the

quarterly data for the period 1986 to 2007. The methodology used in the study is a VAR

cointegration technique for the short run dynamics and vector error correction model (VECM)

for the long run dynamics, and before these first checking the causality effect among the

variables using a Granger causality test. The paper concludes that exchange rate fluctuation and

oil price shocks have effects on the real GDP of Nigeria. The evidence was also provided that

there will be a 7.72% increase in the real GDP with regard to a 10% increase in the crude oil

prices in the long run.

Ukoha (2007) conducted a study for the purpose of establishing a quantitative relationship

between inflation, Nigerian agricultural policies and agricultural commodities relative price

volatility. The data for the study is taken from federal office of statistics, rural development and

federal ministry of agriculture and central bank of Nigeria for the period 1970-2003. The

methodology used in the study is the (ADF) augmented Dickey Fuller test to determine the

time series properties and error correction models were also used for estimation. The findings of

the study conclude that there is a positive significant effect of inflation on long run and short run

crop price variability.


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Chapter 3

Methodology


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3.1 Methodology

In this chapter of the study we discuss the whole method on which this research is based. As

what is the research approach and design, what statistical tool or technique is appropriate, the

sample size, hypotheses and also the research purpose for which the research is conducted. Here

we take personal consumption expenditure (PCE) as the dependent variable and oil and major

and minor crop prices as the independent variable.

3.2 Quantitative research approach

The main focus of this study is to examine the impact of commodity price volatility on PCE

inflation. Here the quantitative approach of research is used due to the need for interpretation and

measurement of numerical data and also due to the need of predicting causal relationship

between the dependent and independent variables. Quantitative approach of research is suitable

for this study because here we are trying to check and confirm the relationship between

commodity prices and PCE inflation.

3.3 Co relational research design

The research design suitable for this study is the Correlational research design which can help in

determining the association between the variables, how the independent variable is related to the

dependent variable and what will be the impact on the dependent variable with a change in the

independent variables. Here in this study we are trying to find out the association between the

commodity price movements and PCE inflation.


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3.4 Data

In this study we use yearly data of 50 years from the period 1961 to 2010. The data source used

in the study is the secondary data source. The data is collected from sources like federal bureau

of statistics, State bank of Pakistan and economic survey of Pakistan economy.

3.5 Statistical technique

The statistical technique used in this study is the multiple regression analysis to examine the

causal relationship between the commodity prices and PCE inflation by estimating the regression

parameters.

3.6 Hypothesis

The hypothesis that is to be tested in the study (Ho = null hypothesis, H1 = alternative

hypothesis) is as follows:

Ho: There is no relationship between oil and crop price movements and PCE inflation

H1: There is a significant relationship between oil and crop price movements and PCE inflation

3.7 Model

For the purpose of this research we are taking personal consumption expenditure as the

dependent variable while oil and crop prices as the independent variables. We can

mathematically represent the regression model as follows:

PCE = + OIL + MJCROP + MNCROP+


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PCE = Personal consumption expenditure

= It is the constant effecting PCE

Coefficient of oil price

Oil = Oil price

= Coefficient of major crop price

MJCROP = Major Crop price

= Coefficient of minor crop price

MNCROP= Minor Crop Price

= Standard error

3.7.1 Personal consumption expenditure (PCE)

The overall measure for the prices of goods as well as services that are being purchased by

individuals, households and non-profit organizations is called (PCE) personal consumption

expenditure.


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3.7.2 Oil price

Includes the relative prices of various type of oil used in Pakistan such as crude oil, Kerosene

and others etc.

3.7.3 Major Crop price

Include prices of all type of major crops that are available in Pakistan like Wheat, Rice, Cotton

etc.

3.7.4 Minor Crop price

Include prices of all type of minor crops that are available in Pakistan like Onion, Chillies, Potato

etc.


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Chapter 4

Data Analysis


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This is the process of collecting data and then transforming it as per our study for the purpose of

demonstration in order to achieve results by analytical and logical reasoning. The information

and results acquired from this chapter will also help in concluding the better recommendations

which will subsequently make this study useful for the readers in respect of making better

decisions and finding better alternatives and solutions in this regard. This analysis is based on the

yearly data of Pakistans crop prices, personal consumption expenditure and international oil

prices. The statistical technique used is multiple regression.

4.1 Descriptive Statistics

This table shows the basic summary relating to the data for providing more effective

understanding of the features and range of data.

Table 4.1 Descriptive Statistics:

Sr. Variables N Mean Std Dev Minimum Maximum

1 Personal ConsumptionExpenditure

50 0.0762 0.1173 -0.5434 0.3473

2 Oil Price 50 0.0239 0.2287 -0.6419 0.5788

3 Major Crop Price 50 0.0554 0.1280 -0.4699 0.2301

4 Minor Crop Price 50 0.0575 0.1981 -0.5921 0.6857

Table 4.1 shows the (N) sample size for the dependent and all independent variables that are

PCE, oil price and major, minor crop prices. There are 50 observations taking the yearly data of

50 years of all variables for the period 1961-2010. We have also use the log-difference

transformation in the variables for the betterment of results and to minimize the residuals for the

purpose of minimizing the error values in the data.

This table also represents the minimum and maximum values in the data for all the variables, the

mean or the overall average of the values in the data and also the standard deviation which tells

us how the values deviates from the mean of the data for each variable.


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4.2 Correlation Coefficients

4.2 Pearson Correlation Coefficients

PersonalConsumptionExpenditure

Oil Price Major CropPrice

Minor CropPrice

Personal ConsumptionExpenditureCorrelation

P-value1.000 0.283

(0.023)0.799

(0.000)0.515

(0.000)

Oil PriceCorrelation

P-value0.283

(0.023)1.000 0.107

(0.230)0.284

(0.023)

Major Crop PriceCorrelationP-value

0.799(0.000)

0.107(0.230)

1.000 0.370(0.004)

Minor Crop PriceCorrelation

P-value0.515

(0.000)0.284

(0.023)0.370

(0.004)1.000

This table represents the coefficient of correlation that tells us the level of association among our

variables of the study. Major and minor crop prices show a very strong relationship with PCE

especially the major crops with a 0.799 correlation while the minor crops with a correlation of0.515. As Pakistan being an agricultural country so its crop prices are closely associated with the

consumer prices specially the food prices. Oil price though have a low correlation with PCE but

is still associated with the consumer prices, considering the facts that Pakistan is an oil importing

country and the input share of oil products in an economy.

The p-value represents the significance level of predictors (oil and both crops) with the

dependent variable PCE.


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4.3 Multi Collinearity Diagnostics

4.3 Multicollinearity Diognostics

Variables Tolerance VarianceInflationFactor

Eigenvalue ConditionIndex

PersonalConsumptionExpenditure

- 0 1.866 1.000

Oil Price 0.920 1.087 0.957 1.396

Major Crop Price 0.863 1.159 0.667 1.672

Minor Crop Price 0.803 1.245 0.509 1.915

The above table shows the multicollinearity diagnostics. Both variance inflation factor (VIF) and

the tolerance value gives the same information as tolerance=1/VIF. All the tolerance values are

higher than the value of (1-Adj R square) which is (1-0.696=0.304) and all the (VIF) values are

lower than 10 which show the non existence of multicollinearity in the data. As all the variables

have input shares in different consumer products as oil prices have impacts on transportation

costs, energy influences etc while major crop prices have impacts on food prices, clothing etc

and minor crops have other products like tobacco etc.

Eigenvalues represents the variance explained by the linear combinations or the contribution of

the variables in case of any collinearity in the model. While condition index is the basic function

for eigenvalues. The first eigenvalue on the dependent variable tells the variance explained by all

the predictors while all other eigenvalues explains no additional variance in the model.


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4.4 Result Summary

4.4 Table Result Summary

Variables Beta t - stat P-value VIF

Constant 0.031* 3.117 (0.003) 0

Oil price 0.076** 1.790 (0.080) 1.087

Major crop price 0.646* 8.311 (0.000) 1.159

Minor crop price 0.126* 2.409 (0.020) 1.245

Adjusted R square 0.696

Durbin Watson 2.356

P-value (0.5111)

F-stat 38.319

P-value (0.000)

The above table basically represents the obtained results after running multiple regression

statistical technique first on (SPSS) Standard Procedures for Social Sciences.

Adjusted R square value (0.696) in the table represents that the impact of change in oil prices and

crop prices on the level of PCE in Pakistan will be up to 69.6 % among a 100. It is quite

justifiable because of the fact that Pakistan is a consumption oriented country and the agriculturalcrops are one of the major inputs in the economy as the price of wheat, cotton and sugar cane

have direct impacts on the prices of (Roti, clothing and sugar). Similarly oil prices also seemed

to be a basic input in the economy as any fluctuation in the price of oil will directly affect the

price of petroleum products, energy prices, and transportation costs.

Beta coefficients represent the impact of change in the independent variable to the dependent

variable. Therefore, the beta coefficient of major crop price shows the highest value (0.646) as it

is directly related to the price of consumer products. While the beta coefficient of oil and minorcrop prices have a lower value in comparison to the major crop price that is (0.031 & 0.076).


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T-statistic measures the relative strength of independent variables to predict the dependent

variable and is sometimes more reliable than the coefficient of regression because it also takes

the error into account. As in our study major crop price have a t-stat value >1.96 and a P-value

1.96 and P-value


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Chapter 5

Conclusion


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5.1 Findings

This research answers many of the questions and reveals facts that were previously uncovered

especially related to the Pakistan economy. The first and the foremost finding is the strong

relationship between the oil price shocks and the personal consumption expenditure. This is

eventually contributing to the level of PCE inflation in the economy. Because continuous

increases in the international oil prices causes Pakistan in the form of high transportation costs,

influence energy and fuel prices (Hobijin 2008), effects also leads to higher imports and

obviously negative balance of payment, contributing to a lower GDP (Hamilton 1983,1996,2003)

and since a lower level of economic growth and moreover the economic development.

Secondly the study also provides clear evidence about the relationship between the crop price

movements and personal consumption expenditure. Similar like oil, crops both major and minorare one of the most important and major inputs in the economy. So rise in the price of crops will

also have almost the same effect on level of PCE inflation as oil price shocks have. There will be

a decrease in the purchasing power of people, ultimately as a result of increased level of core

inflation. Hence, such effects leads to a far more difficult situation for the Pakistan economy

with increasing economic and social problems like unemployment, increasing crime rate,

increasing suicidal rates and frustration in the economy.

5.2 Recommendation

Government of Pakistan shall take necessary steps for the purpose of controlling the level of

PCE inflation in the economy to ensure the betterment of people and the economy as a whole by

using the following measures:-

Controlling the relative crop prices either by using a price ceiling Or by cutting off the production cost through effective usage of technological

advancements and modern modes of agriculture

For the purpose of minimizing the dependency of Pakistan on oil as being an oilimporting country, oil consumption can be decreased by developing some alternate

energy sources like coal, solar power and also wind power etc.


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Make the effective use of crops as being our major source of export for contributingtowards collective growth of economy.

5.3 Future Recommendation

For a future and continuing research regarding the scope of this research can be to focus more on

the impacts that an increasing level of PCE inflation can incur on the economy of Pakistan. As

the current research identifies the major reasons for rise in PCE inflation but does not pay a

detailed level of attention to the counter effects of the same. Similarly a future researcher can

also focus on to investigate some other factors that are contributing towards the increase in PCE

inflation.


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Bibliography

Abbas, S. K. (2009). Do money or oil and crop productivity shocks lead to inflation: The case of

Pakistan.Munich Personal Repec Archive .

http://mpra.ub.uni-muenchen.de/15223/1/MPRA_paper_15223.pdf

Abdullah, M., & Kalim, P. D. (2009). Determinants of food price inflation in Pakistan.

http://www.umt.edu.pk/icobm/proceedings/pdf/Paper6.pdf

Aliyu, S. U. (2009). Impact of Oil Price Shock and Exchange Rate Volatility on Economic

Growth in Nigeria: An Empirical Investigation.Research Journal of Internatonal

Studes , Vol. (11): 4-15.

Bullard, J. (2011). Commodity prices, inflation targeting and US monetary policy.

http://research.stlouisfed.org/econ/bullard/pdf/Bullard_Cape_Girardeau_24_May_2011.p

df

Chou, K.-W., & Tseng, Y.-H. (2011). Pass-Through of Oil Prices to CPI Inflation in Taiwan.

International Research Journal of Finance and Economics , Vol. (69): 73-83.

Edelstein, P. (2007). Commodity prices, inflation forecasts, and monetary policy.

http://sitemaker.umich.edu/pedelstein/files/chapter1.pdf

Evans, C. L. (2011). What are the implications of rising commodity prices for inflation and

monetary policy.

http://www.chicagofed.org/digital_assets/publications/chicago_fed_letter/2011/cflmay20

11_2 86.pdf

Ghalayini, L. (2011). The interaction between oil price and economic growth. Middle Eastern

Finance and Economics , Vol. (13): 127-141.

Hakro, A. N., & Omezzine, A. M. (2010). Macroeconomic effects of oil and food price shocks to

the Oman economy.Middle Eastern Finance and Economics , Vol. (6): 72-90.


37/44


Helmy, H. E. (2010). Inflation dynamics in Egypt: Does Egypt's trade deficit play a role? Middle

Eastern Finance and Economics , Vol. (8): 6-25.

Hobijin, B. (2008). cammodity price movements and PCE inflation. Current Issues in Economics

and Finance, Vol. 14(8): 1-7.

Hsing, Y. (2007). Impacts of Higher Crude Oil Prices and Changing Macroeconomic Conditions

on Output Growth in Germany. International Research Journal of Finance and

Economics , Vol. (11): 134-140.

Huang, A. Y., & Tseng, Y. H. (2010). Is Crude Oil Price Affected by the US Dollar Exchange

Rate?International Research Journal of Finance and Economics , Vol. (58): 109-120.

Hussain, M. F., Anwar, S., & Hussain, Z. (2006). Economics of Sugarcane Production in

Pakistan: A Price Risk Analysis. International Research Journal of Finance and

Economics ,Vol. (4): 70-77.

Hussain, Z., Awan, H., Hussain, I., Farhan, M., & Haq, I.-u. (2006). Demand for money in

Pakistan. International Research Journal of Finance and Economics , Vol. (5): 209-

218.

Jamali, M. B., Shah, A., Soomro, H. J., Shafiq, K., & M.Shaikh, F. (2011). Oil Price Shocks: A

comparative study on the impacts in purchasing power in Pakistan. Modern Applied

Science, Vol. 5(2): 192-203.

Jin, G. (2008). The Impact of Oil Price Shock and Exchange Rate Volatility on Economic

Growth: A Comparative Analysis for Russia Japan and China.Research Journal of

International Studies , Vol. (8): 98-111.

Kelly, A. (2011). US inflation pressure increasing.

http://www.nab.com.au/wps/wcm/connect/7f979e804716b3a8b748f76e6a46f451/USInfla

tionMar11.pdf?MOD=AJPERES&CACHEID=7f979e804716b3a8b748f76e6a46f451


38/44


Manzoor, M. M., Siddiqui, A. A., Sattar, A., Fahim, M., & Rasheed, S. (2011). Impact of

Inflation on Household Consumption-A Case of Pakistan.International Research Journal

of Finance and Economics , Vol. (68): 161-167.

Mohammad, S. D. (2010). The impact of oil prices volatility on export earning in Pakistan.

European Journal of Scientific Research, Vol. 41(4): 543-550.

Morgan, J. P. (2011). Import prices are pushing up US core inflation.

http://s3.amazonaws.com/files.posterous.com/vnl/E3ix0BfS4YllflxZuGyJo0SE7X9JrJiv

HuSioXmYnahFKh1DJfCZcMTWj3hc/Import_prices_are_pushing_up_U.pdf?AWSAcc

essKeyId=AKIAJFZAE65UYRT34AOQ&Expires=1313723014&Signature=zUMB9HI

HukB5IA%2FqLdbLOc47QrA%3D

Nosheen, M., Naqvi, S. N., Shahzad, Q., & Khan, M. T. (2010). The impact of structural

adjustment on economic performance (Cross Country Anlysis). International Research

Journal of Finance and Economics , Vol. (57): 126-139.

Odior, E. S., & Banuso, F. B. (2011). Macroeconomic volatility and private consumption

expenditure: Implication for household welfare.European Journal of Social Sciences,

Vol. 20(2): 319-335.

Papapetrou, E. (2009). Oil Price Asymmetric Shocks and Economic Activity: The Case of

Greece. 1-18.

Rabbani, G., Haque, A., & Khalek, A. (2009). Dynamic Model for Price of Wheat in

Bangladesh.European Journal of Social Sciences, Vol. 10(2): 254-263.

Reicher, C. P., & Utlaut, J. F. (2011). The effect of inflation on real commodity prices.

http://kms1.isn.ethz.ch/serviceengine/Files/ISN/130111/ipublicationdocument_singledoc

ument /d367c4ed-f7c0-4d43-bba4-9fd1ec177e66/en/kwp-1704.pdf

Syed, N. I. (2010). Measuring the impact of changing oil prices and other macro economic

variables on GDP in the context of Pakistans economy. International Research

Journal of Finance and Economics , Vol. (52): 40-49.


39/44


Trung, L. V., & Vinh, N. T. (2011). The Impact of Oil Prices, Real Effective Exchange Rate and

Inflation on Economic Activity: Novel Evidence for Vietnam.DP2011-09 , 1-29.

Ukoha, O. O. (2007). Relative Price Variability and Inflation: Evidence from the Agricultural

Sector in Nigeria.African Economic Research Consortium , 1-28.

Wang, X. (2010). The relationship between economic activity, stock price and oil price:

Evidence from Russia China and Japan. International Research Journal of

Finance and Economics , Vol. (60): 102-113.


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Appendix


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Regression

Descriptive Statistics

Mean

Std.

Deviation N

PCE .076264 .1173819 50

OIL .023973 .2287771 50

MjCROP .055421 .1280947 50

MnCROP .057510 .1981231 50

Correlations

PCE OIL MjCROP MnCROP

Pearson

Correlation

PCE 1.000 .283 .799 .515

OIL .283 1.000 .107 .284

MjCROP .799 .107 1.000 .370

MnCROP .515 .284 .370 1.000

Sig. (1-tailed) PCE . .023 .000 .000

OIL .023 . .230 .023

MjCROP .000 .230 . .004

MnCROP .000 .023 .004 .

N PCE 50 50 50 50

OIL 50 50 50 50

MjCROP 50 50 50 50

MnCROP 50 50 50 50


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Variables Entered/Removedb

Model

Variables

Entered

Variables

Removed Method

1 MnCROP,

OIL,

MjCROPa

. Enter

a. All requested variables entered.

b. Dependent Variable: PCE

Model Summaryb

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

Durbin-

Watson

1 .845a .714 .696 .0647653 2.356

a. Predictors: (Constant), MnCROP, OIL, MjCROP


ANOVAb

Model

Sum of

Squares df Mean Square F Sig.

1 Regression .482 3 .161 38.319 .000a

Residual .193 46 .004

Total .675 49

a. Predictors: (Constant), MnCROP, OIL, MjCROP



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Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

95% Confidence

Interval for B

Collinearity

Statistics

B

Std.

Error Beta

Lower

Bound

Upper

Bound Tolerance VIF

1 (Constant) .031 .010 3.117 .003 .011 .052

OIL .076 .042 .147 1.790 .080 -.009 .160 .920 1.087

MjCROP .646 .078 .705 8.311 .000 .490 .803 .863 1.159

MnCROP .126 .052 .212 2.409 .020 .021 .230 .803 1.245

a. Dependent Variable:

PCE

Collinearity Diagnosticsa

Model

Dimen

sion Eigenvalue

Condition

Index

Variance Proportions

(Constant) OIL MjCROP MnCROP

1 1 1.866 1.000 .11 .06 .12 .13

2 .957 1.396 .17 .65 .07 .02

3 .667 1.672 .60 .20 .11 .30

4 .509 1.915 .12 .10 .70 .55a. Dependent Variable: PCE


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Residuals Statisticsa

Minimum Maximum Mean

Std.

Deviation N

Predicted Value -.333514 .258131 .076264 .0992006 50

Residual -

2.098561

8E-1

.1391961 .0000000 .0627514 50

Std. Predicted

Value-4.131 1.833 .000 1.000 50

Std. Residual -3.240 2.149 .000 .969 50

a. Dependent Variable: PCE

umair mazher thesis

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