# Inflation And Oil Prices In Malaysia

**Published:**
**Last Edited:**

**Disclaimer:** This essay has been submitted by a student. This is not an example of the work written by our professional essay writers. You can view samples of our professional work here.

Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of UK Essays.

It is observed that more than a decade ago, crude oil prices were between $20 to $40/barrel with its ups and downs throughout the late 90s. The price went up slightly before dropping again after the 1997 Asian financial crisis. In the beginning of the 21st century however, the prices started to climb to almost double that to the average value of $40. Volatility is high as by 2002 the value dropped back to about $20 right after the September 11 Attacks before surging up again almost immediately. It can be said that from 2003 onwards, the price of crude oil throughout the world began to steadily ascend higher and higher over the few years till late 2006. This is mainly due to concerns on exhausting oil-wells throughout the world, the post US-Iraq war situation, OPEC's stand on providing the world with limited supply with its reason to preserve resources, increasing growth and dependency on consumption of crude oil in the world and more. All of these add into the weight and urgency that spiked conspiracy and panic into the industry, added that market players took advantage of the situation to further propel prices into the sky.

By 2007, the bubble for the US Sub-prime loan crisis burst and sent a shock wave that affected cash flow in world economies with a shortage of funds. A temporary sharp drop occurred in the prices of crude oil. Within a period of just a year, speculators, players, investors and suppliers all contributed a part to the worst oil price hike in human history. The price of just an average of $60 per barrel more than doubled to an unbelievable peak of $140 or more per barrel. This has sent the world oil prices scrambling to readjust its value in the market and also affecting every other industry linked to it. As quickly as it came, the prices of crude oil crashed back to about $40 per barrel in less than half a year. The crash was timed, played and expected. However the damage has been done to the world economies affecting overall price increases and devaluation of currencies. The more dependent on crude oil a country is, the bigger the effect it has taken. In short, it is the citizens who suffer from these two back to back sub-prime loan and crude oil price hike crisis.

From then since, the price of crude oil has lingered around $40 to $50 average for awhile as economies embrace recovery before the price of crude oil resumed its steep climb again. The climb hovered around $80 per barrel by early 2010. Up until recently, the price of crude oil continues to hang around $75. It is safe to assume that the 21st century modern crude oil prices would continue to be at least this value of $70 and above with the very simple reason of increasing demand with decreasing supply.

Comparing the prices back a decade ago and ignoring the price hike scenario, today's crude oil prices in per barrel is twice as much what was valued back in the year 2000. This just goes to show how much has changed in just a short span of ten years in this industry and provides a good indicator of what the future of the petroleum industry holds for us. One thing is for certain, it is that the price for crude oil will continue to climb at an increasing rate in the future unless a substitute for our heavy reliance on petroleum could be discovered in time. Otherwise, there is only that much we can prepare ourselves and embrace the ever increasing cost of living linked so heavily back to our grave dependency on the core energy source of the modern world.

## Research objective

This research aspires to validate the public perceptions that the oil price hike does essentially affect inflation. It will analyse the consumer price index (CPI) in Malaysia that would be affected by the movement in oil price by world crude oil price. Thus, the objectives of this research are as follows:

(a) To determine the effect of global oil price shock on Malaysia inflation rate.

(b) To validate the theories and perceptions that oil price movements will affect consumer

products and services, regardless of whether fuel is directly or indirectly used as main input

in the business operations and value chain.

## 1.3 Significance of the research

The outcome from this research will assist Malaysia government to formulate measures in time of economic turmoil due to oil price shocks which include fiscal and monetary policies. This will spur economic development and stabilizing inflation and unemployment rate. On the other hand, this research helps management of companies in Malaysia to be sufficiently prepared for any recurrence of oil price crisis that will impact heavily on the country economic. The companies would need to redirect their business model in preparation for the economic crisis.

## Chapter 2 Literature review

## 2.1 Introduction

Inflation influences the economic growth of a country including those developing, developed or even underdeveloped country. High inflation tends to give negative perceptions on a country as it indicates increase in consumer products price and unemployment rate. We believe that oil price movement is the major cause of Malaysia's inflation. Also, we are keen to discover whether real interest rate, real exchange rate, and money supply would cause more on inflation compared to oil price.

## 2.2 Oil price and inflation

Different researches on the oil price fluctuations had been conducted to discover its effect on certain country economy performance. These findings contributed important decision making of macroeconomic variables which seek to cope with the hiking of petrol price. For instance, Hamilton (1983) found that there is significant correlation between oil price movement and economic expansion. This is supported by Gisser and Goodwin (1986) and also Brown and Yucel (2002), who identified that increase in oil price, will tend to retard economic growth. In addition, Tang et al. (2009) in recent research, found that oil price hike negatively decrease output and investment, while it increases inflation and interest rate in China. 1% increases of oil price is said to decrease the output by 0.38%; 100% increases of oil price will increase 7.34% of Producer Price Index (PPI) in the same month and 11.33% in the following month (Tang et al. 2009). These findings supported our research question and objective on whether oil price hike is one of the major causes of inflation in Malaysia.

In Malaysia, Saari et al. (2008) focused on the effect of local petroleum price on the cost production in agricultural and agro-based sectors. They found that if price of petroleum increases 90%, the cost of production for fishing, forestry and logging, and oil palm primary products industries would increase by 30%, 12% and 7%. In our research, we refine Saari's research by discussing several independent variables and its effects on overall cost of production.

## 2.3 Other independent variables and inflation

Other independent variables besides oil price may give high impact on Malaysia's inflation. Turnovsky and Wohar (1984) found that the causality between money supply and aggregate prices in US is rather neutral from year 1929-1979. In Malaysia itself, empirical studies on inflation and money supply are relatively few. Masih and Masih (1998) discovered a unidirectional causality runs from money supply to inflation rate regardless of the lag structure. Recently, Tang (2004) re-investigated the causal relationship and found that money supply leads aggregate price in Malaysia but there is no evidence showing direct causal effect runs from money supply to inflation over 1970-1998. The Fisher (1930) stated that nominal interest rate should reflect movements in the expected rate of inflation. In his findings, there is no apparent relationship between price change and interest rate in the short run. Correlation coefficient of -0.459 was obtained for British data and -0.289 for United State. This is supported by Lardic and Mirgon (2003) which positively validate Fisher effect on G7 countries for the period 1970-2001.

## Chapter 3 Theoretical Framework

## 3.1 Introduction

We are interested to investigate whether increase in oil price would actually cause inflation and if so, how much of this inflation is actually related back to rising of oil price Theoretically, an oil price increase is assumed to be related to causing inflation, most commonly among day-to-day items and activities such as transportation cost, food price, and other short term dealings. This research study is part of finding out the extent of inflation and hopefully to get a good estimation of how much relationship is there between a 10% rise in oil price contributing to inflation.

Long term dealings should remain unaffected by short term price changes as it will require longer period of time for the inflation of price to kick in before its price increases. One such example would be college and school fees. In fact, a good representation of this theory that we could use would be the very recent 2007-2009 oil price hike which spark worldwide oil demand and price going tremendously high yet pummeling to unbelievably low price per barrel. This has somehow contributed to how we came up with this idea for our research project as we are keen to learn about rising oil price and its effect on inflation as a whole.

## 3.2 Variables

Two very important figures in this research project would be oil price and inflation. Oil price as the independent variable is a very volatile value to determine. We would be using global value in determining how much US Dollars/barrel for oil price, occasionally there might be some numbers in Ringgit Malaysia (RM) for local references especially the ballooning price hike for 2008 in Malaysia, but mostly would be converted into US Dollars/barrel to effectively study the effect of oil price versus inflation in a global scale.

Y (inflation) = f (oil price, money supply, real interest rate and real exchange rate)

Due to the nature of the world economy and everything has a relationship with others, there is more than one independent variable besides oil price, including the total money supply in the market, the real exchange rate and the real interest rate. We will also study how each of these variables interact with inflation as well. Inflation would be the dependent variable in this research project. Inflation is calculated from the consumer price index (CPI) which comprises several categories (as shown in Table 1).

## Table 3.1: Weights of the CPI by major categories in Malaysia

## Categories

## Weights (%)

Food and non-alcoholic beverages

31.4

Alcoholic beverages and tobacco

1.9

Clothing and footwear

3.1

Housing, water and electricity

21.4

Furnishings, household equipment and routine household maintenance

4.3

Health

1.4

Transportation

15.9

Communication

5.1

Recreation services and culture

4.6

Education

1.9

Restaurants and hotels

3.0

Miscellaneous goods and services

6.0

## Total

## 100.0

(Sources: Department of Statistics Malaysia, 2010).

## 3.3 Model

The initial model that was proposed is the Input-Output Price Model. It is proven in past researches that this model defines the relationship between oil price and inflation well enough. However, due to restricted resources and time as well as permission to accessible private data, there is only three students could come up with. It is being suggested that we use a Regression Model towards our data for calculation purposes to support our findings in this research project. And we will only use past proven records of results from the Input-Output Model to verify theories that could come up in this research project.

## 3.4 Expected direction

It is expected that oil price as well as the other independent variables such as money supply, exchange rate and interest rate will have some effect on inflation. An increase in oil price, money supply as well as interest rate is expected to positively affect inflation. While an increase in exchange rate however is expected to negatively affect the inflation rate.

Y (inflation) = f (oil price, money supply, real interest rate and real exchange rate)

## (+) (+) (+) (+) (-)

## Chapter 4 Data and Methodology

## 4.1 The data

The data obtained is time-series data from year 2007-2009. Monthly data includes crude oil commodity prices that classified under world oil price. These data is extracted from the Organization of the Petroleum Exporting Countries (OPEC) and Bloomberg. In addition, monthly consumer price index (CPI) is obtained from the Department of Statistics Malaysia while the data of real exchange rate, real interest rate are taken from Bank Negara Malaysia (BNM) Monthly Statistical Bulletin.

## 4.2 Research methodology

Valadkhani and Mitchell (2002) applied the input-output price model to assess the petroleum price shocks on inflation and household expenditures in Australia. Similarly, Saari et al. (2008) examined the impact of petroleum price on costs productions by disaggregating the components in the costs production into three categories: fishing, forestry and logging, and oil palm primary products industries. In the other hand, Hamilton (1983) used seven-variable vector auto regressions (VAR) system to identify the impact of oil price shock on U.S economy in year 1948-1974. Similarly, Marcelo S. (2005) used identified vector autoregressions (IVAR) to analyse the interaction between interaction between exchange rate and cost production. Masih and Masih (1998) employed the Granger causality test, modified Sims causality test and vector error-correction modelling (VECM) approach to examine the causality direction between money supply and aggregate prices in the Southeast Asia economies For Malaysia, they found that all causality tests are consistently implied that money supply (M1 and M2) Granger causes increase aggregate prices. Lastly, Mitchell-Innes H. A. (2006) used the same method (VAR) and vector error correction model (VECM) to prove that inflation and long-term interest rate moved in the same direction.

## 4.2.1 Hypothesis testing

In our research, quantitative research (regression model) is applied as a method of analysis and interpretation of observation data in order to discover the strength of relationships between independent variables (money supply, real interest rate, real exchange rate, and oil price) and dependent variable (inflation). This statistical method starts with the collection of data which will be implied in regression model to verify null-hypothesis testing. Causal relationships are analysed by manipulating the factors to influence the phenomena of interest. Following is the research hypothesis:

Ho: Oil price is not one of the major causes of inflation from year 2007-2009

H1: Oil price is one of the major causes of inflation from year 2007-2009

## 4.2.2 Regression analysis

In order to verify the hypothesis testing above, regression analysis is applied in estimating the unknown parameters β0, β1, β2, β3, and β4 in the relationship, using the data on oil price, money supply, real interest rate, real exchange rate, and inflation rate.

Y1 = β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε

where Y1 is the inflation rate; X1 is the oil price; X2 is the money supply (M1); X3 is the real interest rate; X4 is the real exchange rate; and ε is the variable representing all other factors that may have direct influence on inflation rate.

In addition, coefficient of determination is implied to identify the significance of independent variables (money supply, real interest rate, real exchange rate, and oil price) on dependent variable (inflation).

R²= {(1/N)*Σ[(xi-x)*(yi-y)]/(σx*σy)}²

Results from the regression analysis and coefficient of determination test shall decide whether or not Null Hypothesis (Ho) will be accepted and reject Alternative Hypothesis (H1). Statistics, tables and graphs will be used to present the results of these methods.

## Chapter 5 Research findings

## Initial Analysis

Initial findings are done based on the graph analysis to identify the relationship between dependent variable (CPI) and independent variables (oil price, money supply, real interest rate and real exchange rate). From the graph, we analysed and identified reasons of those variables fluctuations from year 2007-2009.

## Figure 5.1: Relationship between CPI and Oil Price

From Figure 5.1, it can be observed that inflation has a steady rate of increase over the past three years while oil prices met with a steep increase and peaked at June 2008 before crashing low at December 2008. After that, the value of oil prices increases steadily again.

Much of the fluctuation in the value of oil prices was caused mainly by human speculation and manipulation of the oil market where issues such as limited oil supply, on-going war, terrorism and the sub-prime financial crisis caused the price hike. Despite the high and low prices of oil per barrel around the world, inflation rate was not highly impacted.

## Figure 5.2: Relationship between CPI and Money Supply

From Figure 5.2, it can be observed as usual inflation rate steadily increasing over the years without much interference. However, money supply has been increasing as the gap between inflation and money supply is closing in together over the years. This could be mainly caused by the recent financial crisis which caused governments around the world to panic and quickly come up with programs to reduce the impact of the financial crisis which saw many going bankrupt.

Programs are usually drawing up of hefty sums of cash to be spent by the government or distributed through ways as a mean to have people spend money to keep the economy of the country moving. Billions of dollars worth in bills and bonds are created and countries such as Australia and Singapore even had its government give out Christmas Bonus in cash to encourage their citizens to spend. There is also the usual routine of printing new money each year into the economy. All of these and more could have added up to the increasing rate of money supply into the economy.

## Figure 5.3: Relationship between CPI and Interest Rate

From Figure 5.3, real inflation rate sees a steady increase as already mentioned however interest rates have a rather surprising pattern. This shock pattern can be observed at September 2007 when interest rates suddenly spiked to almost double its usual rate from 3.60% to 6.61% in the difference of a month. There was not much effect on inflation rate but this could be due to time lag for it to have effect.

Ironically, it was this same period that the sub-prime financial crisis really started to accumulate in a wider problematic scale. Much could be questioned on why did the interest rates spiked all of a sudden. Yet in the aftermath of the crisis, we are seeing a steady decline on interest rates around the world. This is mainly because interest rates are being lowered now to help accelerate recovering economies by providing cheaper access to funding. Typical, this low interest rate attraction was what started the bubble for this crisis in the first place back in the year 2001 right after the September 11 Attacks.

## Figure 5.4: Relationship between CPI and Exchange Rate

Inflation rate is once again here, only seeing a slight peak when oil prices jumped over everyone's expectations. There is not much to be said for inflation rate with exchange rate as they both seem to be almost a nice straight line on the graph. A closer look into Figure 5.4 however tells us that inflation rate has been increasing steadily over the years while on the other hand exchange rates have seen an increase and decrease over the years instead.

Exchange rate has not seen much big movements. Reasons could be that exchange rates around the world are extra sensitive and its value will react accordingly with all other exchange rates to find its correct value thanks to rapid arbitragers. Another reason could be due to economies around the world encouraging lower exchange rates to increase trading among countries. This would help the weakened economies recover better.

## 5.2 Empirical result and analysis

## Table 5.1: Mean and standard deviation

## Mean

## Std. Deviation

## N

## CPI

109.733

3.4206

36

## Oil Price

69.75

25.368

36

## Money Supply

888203.088

71807.6196

36

## Real Interest Rate

3.4600

.66410

36

## Real Exchange Rate

3.428771

.1332732

36

From Table 5.1, the number of observation (N) represents sample size collected from year 2007-2009 (12 months x 3 years). Smaller sample size tends to increase variability of the distribution. N equals 36 which is larger than 30, hence the difference is negligible. We assumed that the distribution is normal and represent the population.

## Table 5.2: Skewness and kurtosis

N

Sum

Mean

Skewness

Kurtosis

Statistic

Statistic

Statistic

Std. Error

Statistic

Std. Error

Statistic

Std. Error

CPI

## 36

## 3950.4

## 109.733

## .5701

## -.116

## .393

## -1.570

## .768

Valid N (listwise)

## 36

From table 5.2, the skewness and kurtosis are -0.116 and -1.570. Negative skewness indicates that the distribution skews to the left. Meanwhile, negative kurtosis indicates that the distribution has shorter tail. Hence, we concluded that the data is not normal distributed and asymmetrical.

## Table 5.3: Variables coefficients

## Model

## Unstandardized Coefficients

## Standardized Coefficients

## t

## Sig.

## 95.0% Confidence Interval for B

B

Std. Error

Beta

Lower Bound

Upper Bound

1

## (Constant)

13.325

9.178

1.452

.000

-5.392

32.043

## Oil Price

.089

.013

.664

6.812

.000

.063

.116

## Money Supply

3.911E-5

.000

.821

11.267

.000

.000

.000

## Real Interest Rate

.445

.369

.086

1.205

.000

.308

1.197

## Real Exchange Rate

-.716

.509

.612

6.263

.000

-.898

.834

a. Dependent Variable: CPI

From Table 5.2, we can observe that:

Oil price:

Oil price has positive coefficient, indicating that this variable has positive relation with CPI.

When oil price increases by USD1, the CPI will increase by 0.089% in one month on average, while other variables being constant.

The p-value for oil price is 0.00, which is smaller than the 5% level of significance used in the test.

Money supply:

Money supply has positive sign of coefficient, indicates that this variable has positive relation with CPI.

If the rate of money supply increases by MYR1, the CPI will increase by 3.911E-5% in one month on average, while other variables being constant.

The p-value for money supply is 0.00, which is smaller than the 5% level of significance used in the test.

Real interest rate:

Real interest rate has positive coefficient, showing positive relation with CPI.

When interest rate increases by 1%, the CPI will tend to increase by 0.445% in one month on average, while other variables being constant.

The p-value for exchange rate is 0.00, which is smaller than the 5% level of significance used in the test.

Real exchange rate:

Real exchange rate has negative coefficient, indicating that this variable has negative relation with CPI.

When exchange rate increases by 1%, CPI will decrease by 0.716% in one month on average; while other variables being constant.

The p-value for exchange rate is 0.00, which is smaller than the 5% level of significance used in the test.

All independent variables have significant value (sig.) smaller than 0.05. This indicates that all variables represent large level of statistical significance in the model. Therefore, equation for the model would be:

CPI = 13.325 + 0.089 Oil Price + 3.911E-5 Money Supply + 0.445 Real Interest Rate

- 0.716 Real Exchange Rate

## Table 5.4: Adjusted R square

## Model

## R

## R Square

## Adjusted R Square

## Std. Error of the Estimate

1

.951a

.904

.891

1.1281

a. Predictors: (Constant) Oil Price, Money Supply, Interest Rate, Exchange Rate

From Table 5.3, adjusted R square is 0.891, indicating that all variables (oil price, money supply, interest rate, exchange rate) explain 89.1% of the variation in CPI. There is a strong positive correlation between the independent variables and dependent variable. Hence, it is considered that the variables fit closely into the model and are more likely to predict CPI's movement.

Coefficient of Variation (CoV) is calculated to evaluate model's goodness of fit. From Table5.3, the standard error of the estimate is 1.1281. From Table 5.1, the mean CPI is 109.733. The calculation of CoV is as follow:

Coefficient of Variation = (SE)/ (Mean Price) x 100%

= 1.1281/ 109.733 x 100%

= 1.028%

The coefficient of variation of 1.028% indicates that the average forecast error is 1.028% of average CPI. The model is considered as good model as its CoV is lesser than 5%.

Table 5.5: ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

370.069

4

92.517

72.699

.000a

Residual

39.451

31

1.273

Total

409.520

35

a. Predictors: (Constant), Exchange Rate, Interest Rate, Money Supply, Oil Price

b. Dependent Variable: CPI

Hypothesis:

Ho: Oil price is not the major cause of Malaysia inflation from year 2007-2009

H1 : Oil price is one of the major causes of Malaysia inflation form year 2007-2009

From Table 5.4, the significance value is 0.00 which is smaller than the level of the significance (0.05). Therefore, the null hypothesis (Ho) will be rejected. Oil price is considered as the major cause of Malaysia inflation from year 2007-2009.

## Table 5.6: Correlation between CPI, oil price, money supply, interest rate, and exchange

## rate.

CPI

Oil Price

Money Supply

Interest Rate

Exchange Rate

Pearson Correlation

CPI

1.000

.145

.857

.400

-.226

Oil Price

.145

1.000

-.052

.188

-.803

Money Supply

.857

-.052

1.000

-.601

.200

Interest Rate

.400

.188

-.601

1.000

-.193

Exchange Rate

-.226

-.803

.200

-.193

1.000

Sig. (1-tailed)

CPI

## .

.199

.000

.008

.092

Oil Price

.199

## .

.381

.136

.000

Money Supply

.000

.381

## .

.000

.122

Interest Rate

.008

.136

.000

## .

.130

Exchange Rate

.092

.000

.122

.130

## .

N

CPI

36

36

36

36

36

Oil Price

36

36

36

36

36

Money Supply

36

36

36

36

36

Interest Rate

36

36

36

36

36

Exchange Rate

36

36

36

36

36

From table 5.5, it can be observed that:

Correlation between CPI and oil price is 0.145, indicating positive relationship between both variables.

Correlation between CPI and money supply is 0.857, indicating positive relationship between both variables.

Correlation between CPI and interest rate is 0.400, indicating positive relationship between both variables.

Correlation between CPI and exchange rate is -0.226, indicating negative relationship between both variables.

The largest Pearson Correlation coefficient is to be found between CPI and money supply, which is 0.857.

The Squared Correlation figure of 0.735 (0.857²) is smaller compared to the adjusted R² of equation model, which is 0.891. Thus, the correlation between CPI and money supply does not show any signs of collinearity problem with the model.

Since the biggest available coefficient between pairs of independent variables is not a problem, hence the equation model does not have multi-collinearity problem.

## 5.3 Discussion

From the result, it is proven that oil price does influence the inflation fluctuations. Oil price has positive relationship with CPI at 5% level. USD1 increase in oil price tends to increase 0.089% of CPI. Oil price influences most of the cost of goods and services in the market including cost of production and transportation. As a developing industrial country, Malaysia relies heavily on the import of crude oil as source of energy. Significant increase of oil price tends to cause hyperinflation and therefore effect the development of the country.

The results show that other variables such as interest rate, money supply and exchange rate do influence the inflation rate as well. From the analysis, money supply has strong positive relationship with Malaysia CPI between years 2007-2009. Hawtrey (1923) stated that the public holds undue proportion of cash balances with respect to their income when money supply increases. Hence, they tend to increase the level of spending. This is proven by the Quantity Theory of Money. When amount of money in economy increases faster than the growth in the level of future output, this will increase the market price level and therefore causing inflation. Malaysia government uses various measures of money supply as intermediate targets to maintain the stability of inflation.

Meanwhile, interest rate does influence inflation. 1% increase of interest rate would tend to increase 0.445% of inflation rate. Malaysia as a relatively small open economy has its interest-rate policy closely linked to the fluctuations of inflation. This is essential to prevent excessive funds inflow and outflow which could disrupt the balance of payments and country's economic development. When interest rate increases, public tends to save more. Money will flow to secured debt market while funds availability in the market would be rationalised. Banks lending rate will increase and have cascading effect of higher production cost- higher inflation rate.

Lastly, it is shown that exchange rate has negative relationship with CPI. A depreciation of exchange rate means that the local currency is relatively weaker compared to foreign currencies. Exported goods and services to foreign countries are now relatively cheaper. This leads to increase in exporting of local goods and services and therefore contribute to higher inflation rate in the country. From the analysis, 1% increase in exchange rate would eventually decrease the CPI by 0.716%.

## Chapter 6 Summary and conclusion

The world has witnessed the impact of oil price hike which had wide coverage in the media and also by observing the great fluctuation of petroleum products such as diesel, petrol and gasoline throughout the year 2007-2009. This research is being conducted in order to discover whether oil price is one of the major causes of Malaysia inflation.

From the empirical results and analysis, we are confident that oil price does affect the inflation rate in Malaysia. This result is proven by the previous research done by Tang et al. (2009) which showed that increase in oil price hike increase the inflation and interest rate in China. In addition, our findings have supported Saari et al. (2008) research that local petroleum price would increase the cost production in agricultural and agro-based sectors which could cause higher inflation rate.

Looking back into past studies and history, increase in oil price has never been good to the economy. Oil price shock would spur inflationary pressure and dampen the economic growth. Based on the data from Malaysian Department of Statistics (2010), we found that Malaysia is an oil importer with an average of 7.9 millions metric tons per year. In order to curb with the inflation and ease the burden on consumers and low income citizens, Malaysia government has been subsidizing petrol since June 2005. The price of petrol in Malaysia is reviewed from time to time by using world crude oil price as the benchmark. In year 2007, government spent RM8.65 billion with the crude oil prive averaging USD78/barrel and it reached RM18.35 billion in 2008 when the oil price hit USD127/barrel. Hence, with the research conducted, it is hoped that the results and analysis would give a clear picture on how much would the oil price hike affect the inflation in Malaysia.

Besides oil price, we have proven that other variables including money supply, real interest rate and real exchange rate do cause inflation as well. More important, we discovered that real interest rate and real exchange rate impose higher influence on inflation compared to oil price hike. From these findings, policy-makers should take oil price and those variables in consideration in dealing inflation rate effectively.

## Chapter 7 Limitation

Up until now, the results of the research project is prominent to a point which we can draw conclusions, interpretations and recommendations from it. However as with all things in life, nothing is perfect in this world. The results of this research might reliably prove a point but the advice should be taken with caution as these results are produced with several limitations facing our team.

One such limitation is the lack of a complete and full business cycle which roughly equals up to ten to twelve years of recorded data. In this research itself, we are only dealing with a short term time span of three years consisting of thirty six months of records ranging from January 2007 to December 2009. The reason for such a short time span was due to the difficulty in obtaining older data from reports such as CPI, exchange rates and interest rates. Such data are either not made available on the internet or from our local library and in order to obtain data from the early 90s, would require obtaining permission from local authorized government record centres which could take weeks to months for approval. Dealing with the limited time we had to conduct this research project, we choose to narrow down our data collection to a period within our grasp. With the internet expanding rapidly in the 20th century, it was possible to obtain much more information from the internet nowadays. Thus with the limited data set to the span of three years and not illustrate an entire business cycle, the results might be skewed towards the happenings of just this three years and due to this it might not provide a proper and reliable representation of the behaviour between the components of our research accurately.

Another limitation we face in this research project is the lack of sufficient variables to represent the entire economy model in action. Such factors missing from the results include but are not limited to GDP, unemployment rates and more. Reasons for these missing factors into the research model vary with each individual factor. For instances, GDP was actually included into the model but was later taken out due to the confusing results it produced which do not sync well with the rest. It was later concluded that our model did not properly model GDP instead and it might be due to the limitations set by the regression model itself. Unemployment rates are also not in this model due to the difficulty in obtaining a reliable source for this data, even more so in a monthly method. Most publishers only publish these rates on a quarter or annual basis. Other factors that play a role such as political stability, social behaviour and technological advancement are extremely difficult to put in a state of calculations that is used to determine the results and effect on the model. There is no proper way to calculate political stability, social behaviour of the community as well as technological advancement as these things are uncontrollable and unpredictable.

The method and model that was used for this research project represents another threat to the integrity of the results for this research as by only using the regression model, we are limiting ourselves to a one-way computer generated result that only calculates in a straight line formula. As past research done by other various researchers around the world have concluded that the regression model while creditable for its reliability does not have sufficient simulation that of in the real world economies to accurately display what would have happen and what had already happened between the component factors and its relationship. The regression model in itself was already a limiting factor on how the results would play out. Hence other models were developed by researchers to more accurately model things like the real world economy. Models such as VAR and the Input-Output Model was suggested and proposed. However it was agreed that there was not sufficient time and resources for us to complete the research project of that magnitude and size for the accuracy desired in one single semester.

Another factor is time lag which means that the data obtained and results acquired do not represent their respective time periods. This is true because a change in oil price today do not immediately affect inflation and commodity prices to change instantly. They require a small time gap to let the other related variables readjust themselves. This small time gap is known here as the time lag which is the difference it would take for one change in a variable to have its full effect affect another variable completely. Example, a person taking medicine in the morning doesn't mean he will recover immediately; it would take some time for the medicine to have its full effect to help the person recover. Hence, an increase in oil price today would require a period of time before its effect can carry forward to other industry increasing their cost of production. Due to short time span in our research data, our results might not fully capture the full effect of time lag between the rate of inflation and the increase in oil price today.

These are just the few main limitations affecting the credit worthiness of this research results and thus a word of advice is to only take the results of this research project as a simple guideline for future similar projects. The recommendations provided here are also affected by these limitations and we believe that there is a lot more room for improvements in the future.

### Cite This Essay

To export a reference to this article please select a referencing stye below: