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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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Commodity Price Movements and PCE Inflation 2
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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Commodity Price Movements and PCE Inflation 3
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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Commodity Price Movements and PCE Inflation 4
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/Policymakers8/22/2019 Umair Mazher Thesis
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Commodity Price Movements and PCE Inflation 6
Chapter 2
Literature review
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Commodity Price Movements and PCE Inflation 7
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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Commodity Price Movements and PCE Inflation 8
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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Commodity Price Movements and PCE Inflation 9
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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Commodity Price Movements and PCE Inflation 10
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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Commodity Price Movements and PCE Inflation 11
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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Commodity Price Movements and PCE Inflation 12
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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Commodity Price Movements and PCE Inflation 13
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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Commodity Price Movements and PCE Inflation 14
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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Commodity Price Movements and PCE Inflation 15
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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Commodity Price Movements and PCE Inflation 16
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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Commodity Price Movements and PCE Inflation 19
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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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
b. Dependent Variable: PCE
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
b. Dependent Variable: PCE
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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