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FORECASTING THE GROSS DOMESTIC PRODUCT GDP) OF MALAYSIA

BRENDA BOPULAS

This

project is

submitted in

partial fulfillment

of

the

requirement

for the

degree of Bachelor ofEconomics

with

Honours

International

Economics

FacultY

of

Economics

and Business

UNlVERSITI

MALAYSIA SARAWAK

2011


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ABSTRAK

MERAMAL KELUARAN DALAM NEGERI KASAR (KNDK) DI

MALAYSIA

Oleh

BRENDA BOPULAS

Kajian ini bercadang untuk mengkaji penentu keluaran dalam Negeri kasar KNDK)

di Malaysia dan kemudian menggunakannya untuk meramal KNDK Malaysia. Ujian

empirikal yang digunakan termasuk ujian kepegunan, ujian kopengamiran Johansen

dan pekali korelasi. Model yang digunakan untuk meramal KNDK ialah model

Autoregressive Integrated Moving Average ARIMA), model asas dan model

Random Walk. Keputusan daripada kajian ini mengesahkan bahawa bekalan wang,

pengeluaran perindustrian, eksports dan perbelanjaan penggunaan isi rumah

mempunyai perkaitan yang kuat dengan KNDK. Seterusnya, kajian ini jug

mengesahkan bahawa untuk meramal KNDK tepat, semua pembolehubah yang

mempunyai perkaitan dengan KNDK perlulah dimasukkan kerana persamaan yang

hanya ada satu pembolehubah atau yang hanya melibat KNDK sahaja akan lebih

kepada menghasilkan ramalan yang kurang tepat.


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ABSTRACT

FORECASTING THE GROSS DOMESTIC PRODUCT (GDP)

O

MALAYSIA

By

BRENDA BOPULAS

This study intends to examine the determinant

o

Gross Domestic Product GDP)

o

Malaysia and use them to forecast the GDP

o

Malaysia. The empirical test that is

used in this study includes unit root test, Johansen cointegration test and correlation

test. The models that are employed are Autoregressive Integrated Moving Average

ARIMA), fundamental models and Random Walk ModeL The results state that

money supply, industrial production, exports and household consumptions have

strong relationship with GDP and in order to forecast the GDP

o

Malaysia, all

variables that are important and could impact the GDP should

be

included. Single

equation model tends to produce less accurate forecast.


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cknowledgement

I would like to grab the opportunity to offer special thank to the organization and all

the people involve that had assisted me in complementing this paper.

First and foremost, I would like to say thank you to my university, University

Malaysia Sarawak UNIMAS) for their support and also effort in ensuring that all o

the third year students would be able to take their final year project as it is one o the

prerequisites in order to enable the students to qualifY for their graduation. I would

also like to thank my faculty, Faculty o Economics and Business FEB) for all their

support and also the resources that they had provided in order for me to successfully

complete my paper.

Secondly, a warm thank you also to my supervisor, Associate Professor Dr Venus

Khim Sen-Liew for the time and patient that he had invested in supervising me all

the way until the completion

o

this paper is made possible. This paper would not be

able to

be

completed without his guidance and advices.

Not forgetting also the lecturers

o

Faculty

o

Economics and Business FEB) that

had teaches me all the fundamental knowledge and concept o economics from

scratches as it was my first time learning economics. The knowledge that I learn all

the while had help me in completing my paper.

Lastly, I would also like to thank all my friends, course mates and family that always

by my side to motivate me and offer encouraging words to me that help me

overcome all the difficulties and tension during the process

o

doing this paper,

without their motivation and trust, I would not

be

able to complete this paper.


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T BLE OF CONTENTS

LIST OF TABLES ................................................................................................ viii-ix

LIST OF FIGURES ...................................................................................................... x

CH PTER : INTRODUCTION

1.0 Introduction ..................................................................................................... 1-2

1 1

Concept o Study ............................................................................................ 2-3

1.1.1 Importance o Economic Forecasting ......................................................

3

1.1.1.1 Individual. ..................................................................................... 3

1.1.1.2 Business ....................................................................................... 3

1.1.1.3 Financial Institution .....................................................................

4

1.1.1.4 Government. ................................................................................. 4

1.1.2 Forecasting Gross Domestic Product GOP) in Malaysia .................... .4-5

1.2 Background o Study ........................................................................................ 5

1.2.1 History and Governance

o

Malaysia .................................................... 5-7

1.2.2 Geography ............................................................................................. 7-8
http:///reader/full/UN4VERS.TJ


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1.2.3 Economy...............................................................................................7-12

1.3

Motivation

o

Study..................................................................................... 12-13

1.4 Problem Statement. ...................................................................................... 13-15

1.5

Objective o Study............................................................................................ 15

1.5.1 General Objective .................................................................................... 15

1.5.2 Specific Objective ................................................................................... 15

1.6 Significance o Study .................................................................................. 15-16

1.7 Structure o Study............................................................................................. 17

CHAPTER : LITERATURE REVIEWS

2.0 Introduction.................................................................................................. 18-19

2.1

Theoretical Framework ....................................................................................

19

2.1.1 Stock Market. ..................................................................................... 19-20

2.1.2 Real Activity ...........................................................................................20

2.1.3 Money Supply .........................................................................................

21

2.1.4 Exchange Rate ...................................................................................21-22

2.1.5 Interest Rate ........................................................................................22-23


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2.1.6 Trade........................................................................................................23

2.1.7 Consumption Expenditure ................................................................ .23-24

2.2

Empirical Testing Procedure ............................................................................24

2.2.1 Specification o Models......................................................................24-27

2.2.2 Forecasting Models .................................................................................27

2.2.2.1 Vector Autoregressive (V AR) ModeL ................................ .28-29

2.2.2.2 Dynamic Stochastic General Equilibrium (DSGE) modeL ...... .30

2.2.2.3 Dynamic Factor Model.. ....................................................... 30-32

2.2.2.4 Univariate Autoregressive Integrated Moving Average

(ARIMA) ..............................................................................32-33

2.2.3 Empirical Method ...................................................................................

33

2.2.3.1 Stationary test. .......................................................................33-34

2.2.3.2 Johansen Multivariate Cointegration Test .............................34-35

2.2.4 Forecast Criteria .....................................................................................

35

2.2.4.1 Information Criteria .................................................................. .36

2.2.4.2 Root Mean Square Error (RMSE) ........................................36-37

2.2.4.3 Mean Absolute Percentage Error (MAPE) ........................... .37-38


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2.2.4.4 Encompassing tests ....................................................................38

2.3 Empirical Evidence .....................................................................................39-43

2.4 Concluding Remarks ..................................................................................43-44

CHAPTER : METHODOLOGY

3.0 Introduction..................................................................................................

57

-58

3.1 Model and Data Description .......................................................................

58-61

3.2 Empirical Testing Procedure ............................................................................

61

3.2.1 Unit Root Tests ........................................................................................62

3.2.1.1 Augmented Dickey-Fuller (ADF) .........................................62-63

3.2.1.2 Phillips-Perron (PP) ...............................................................63-64

3.2.2 Cointegration Test. ............................................................................. 64-65

3.2.3 Forecasting Model. ..................................................................................65

3.2.3.1 ARIMA Model. ....................................................................65-66

3.2.3.2 Fundamental Models .................................................................66

3.2.3.3 Random Walk ModeL ...............................................................66

3.2.4 Model Specification and Diagnostic Checking ......................................67


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3.2.4.1 Normality Test. ...........................................................................67

3.2.4.2 Correlation Test.. .................................................................. 67-70

3.2.6 Forecast Evaluation Criteria ...................................................................70

3.2.6.1 Root Mean Square Error (RMSE) ..............................................70

3.2.6.2 Mean Absolute Percentage Error (MAPE) ...........................70-71

CH PTER

:

RESULTS ND DISCUSSION

4.0 Introduction..................... , ........................................................................... 71-72

4.1

Unit Root Test ...................................................................................................73

4.1.1 Unit Root Test Result. ........................................................................ 73-79

4.2 ARIMA Model ..................................................................................................80

4.2.1 ARIMA Model Forecasting Accuracy ...............................................80-83

4.2.2 Comparison between yearly and quarterly ARIMA model.. ............. 83-84

4.3 Fundamental Models ...................................................................................84-85

4.3.1 Model 1 ............................................................................................86-87

4.3.2 Model 2 .............................................................................................88-89

4.3.3 Model 3 ............................................................................................89-93


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4.3.4 Performance

o

theFundamentalsModels..............................................

93

4.4 RandomWalkModel........................................................................................94

4.4.1 ForecastAccuracyo RandomWalkModel...........................................94

4.5 ComparisonsbetweenARIMA,FundamentalmodelsandRandomWalk

Model................................................................................................................ 95

CH PTER

:

CONCLUSION ND RECOMMEND TION

5.0 Introduction......................................................................................................96

5.1 Summaryo Finding....................................................................................97-99

5.2 PolicyImplicationandSuggestion..................................................................99

5.2.1 PublicExpenditure...............................................................................100

5.2.2 ForeignMarketAccess..........................................................................

101

5.2.3 MonetaryPolicy................................................................................... 102

5.3 Limitation........................................................................................................ 103

5.4 RecommendationforFutureStudies..............................................................103

5.5 ContributiontoStudy.....................................................................................104

5.6 ConcludingRemark................................................................................104-105


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R F R N S

................................................................................................ 106 112

PPENDIX


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LIST OF T BLE

Table 2.1: Summary

o

Literature Reviews ........................................................ .45-56

Table 4.1: ADF results for 1970 to 2010 ................................................................... 76

Table 4.2: PP results for 1970 to 2010 ....................................................................... 77

Table 4.3: ADF results for 1970 to 2008 ................................................................... 78

Table 4.4: PP results for results for 1970 to 2008 ...................................................... 79

Table 4.5: Forecasting Performance

o

ARlMA models with constant (Yearly) ...... 82


o

ARIMA models without constant (Yearly)

.

83

Table 4.7: Johansen Multivariate Co ntegration test for Model

............................

87

Table 4.8: Johansen Multivariate Cointegration test for Model 2 .............................. 88

Table 4.9: Jarque-Bera Normality Test ..................................................................... 90

Table 4.10: Pearson Correlation Test for 1970 to 2010 ............................................. 90

Table 4.11: Pearson Correlation Test for 1970 to 2008 ............................................. 90

Table 4.12: Johansen Multivariate Cointegration test for Model 3 ........................... 92

Table 4.13: Forecasting Performance o Fundamental models ................................. 93


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Table 4.14: Forecasting Performance ofRandom Walk ModeL............................... 94


of

ARIMA, Fundamental Model and Random

Walk Model ............................................................................................. 95

Table AI PP results for results for 1991QI-201OQ4 ................................ Appendix A

Table A2: PP results for results for 1991QI-2008Q4 ............................... Appendix A

Table

Bl

Forecasting Performance

of

ARMA models with constant

(Quarterly) ................................................................................. Appendix B


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LIST OF FIGURE

Figure

1:

Map o Malaysia ...........................................

......

..................... 8

Figure 2: Trend o GOP in Malaysia from 1980-2009 ...............................................

9


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Chapter One

Introduction

1 0 Introduction

This study attempts to forecast the Gross Domestic Product GDP)

o

Malaysia. Forecasting is a process to estimate the future value and it could be done

by a lot o agents which include individual, businesses, financial institution and even

government. The objectives in this study are to determine the determinant o GDP,

to

forecast the GDP and later to evaluate the forecast accuracy. The factors considered

include money supply in which according to theory is positively related to GDP

Arnold, 2008), interest rate which has a theoretically negative relation with GDP

Obamuyi, 2009), exchange rate which is negatively related Rodrik, 2008),

household consumption expenditure, industrial production and also exports which

these three factors according to theory are all positively related the Kogid, Mulok &

Lim, 2010; Fama, 1981; Fama, 1982; Keynes, 1936).

o forecast Malaysia GDP with good accuracy Autoregressive Integrated

Moving Average ARIMA) time series models, fundamental models and random

walk model are estimated. Then, the best forecasting model that could accurately

forecast the GDP will

be

identified.

To preview this study results, the major finding is that broad money, household

consumption expenditure, industrial production and exports are very important to the

economy growth o Malaysia. Moreover, fundamental models performed the best

1


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from ARIMA time series model and random walk model. Random walk model

performed the worst among the three models. However, this is against the spirit o

parsimony that state simple model specification is better (Gilbert, 1995).

Chapter 1 is organized as below. Section 1 1 discuss briefly on the concept o

study. Then, Section

1 2

gives a brief overview

on

the background

o

study. Section

1 3

discusses the significant

o

the study. This is followed by Section 1.4 which

states the motivation o doing the study. After that, Section

1 5

will discuss the

problem statement in the study and Section 1.6 lists out the objectives

o

doing this

study. Finally, Section 1.7 provides the organisation o the study.

1 1 oncept of Study

Forecasting in general is the process

o

estimating the future value

o

a

variable. Forecasting is an extremely complex activity that could influence the

setting up

o

an organization. Forecasts

o

macroeconomic variables are also crucial

to many agents o the economy and that include Central Bank, commercial banks,

investors, speculators, government, policy maker and individual which includes

household. As forecast it is important to most o the agents

o

the economy thus

there are a number o studies done to forecast the economic performance. This

includes studies to forecast the inflation (Serrato, 2006; Mehrotra

&

Sanchez-Fung,

2008; Beechey & Osterholm, 2010), GDP (Krajewski, 2009; Mittnik & Zadrozny,

2004; Lu, 2009; Gupta, 2006), national accounts (Angeline, Banbura

&

Runstler,

2008), interest rate (Bidarkota, 1998; Fletcher

&

Gulley, 1996; Byers

&

Nowman,

1998)), exchange rate (Grossmann & McMillan, 2010; Carriero, Kapetanios &


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Marcellino, 2009 and some other kinds o forecasts. The importances o economic

forecasting in general are discussed below:

1 1 1 Importance

o

Economic Forecasting

1 1 1 1 Individual

For individuals, economic forecast helps them connect to the economic affairs

as they will get brief and fair ideas on how the things are going to be in the future.

Forecasting could help people to be in charge

o

the economic affairs and make

better decision that may help them to avoid losses.

1 1 1 2 Business

As the business environment is constantly changing, forecast could help

company to foresee the future which is important in enabling the management to

change operation at the right time in order to reap greatest benefit. In addition,

forecasting could also avoid the company from losses y making a proper decisions

based on relevant information. This is done by forecasting the economy and market

to establish the pattern o the market, the size and its growth potential. Besides,

forecasting could give them a brief idea on the kinds o government policy that

would be used as businesses performance are also sometimes ties to the policy

introduced by the government.


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1.1.1.3 Financial Institution

Forecasting could help anchor the expectation of

the firms and households

which could help financial institution to become more effective in fulfilling the

demand of the individual and businesses. Besides, forecast publications could also

prevent them from making wrong investment that could result in great losses.

1.1.1.4

Government

Forecasting also plays an important role especially for Government strategic

planning and when

t

is required to do certain long term projects and evaluation

of

the economy. Most ofthe time, forecasting is carried out when there is a need to seek

for aid to decision making and certain planning

in

the future. Especially when

Government intend to introduce new economic policy, it is important to know the

trend for the country economy in order to make sure that the policy introduced

is

suitable with the country s economy situation.

1.1.2 Forecasting Gross Domestic roduct (GDP) in Malaysia

The aim

of

this study is to forecast the GDP

of

Malaysia. There are a lot

of

agencies in Malaysia that forecast GDP. These agencies include Malaysia Institute of

Economic Research (MIER), Bank Negara Malaysia (BNM), Amanah Mutual

Berhad (AMB) and other commercial banks. Producing accurate forecast in

Malaysia is important.

This is important

s

the forecasted value

of

the Gross Domestic Product can

give an overview on how the economy would be behaving in the future and enable


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the policymaker to come up with policies that would suit the economic condition in

the future. This eventually could prevent Malaysia to encounter any kind

of

crisis as

we would already have an overview on the country economy and policy could be

implemented earlier as to counter these problems. For example the financial crisis in

1997 to 1998 where with forecasting the effect of debt if Malaysia borrow from

International Monetary Fund (IMF) it is found out that Malaysia would not be able

to finish paying it for a very long time s the interest is very high thus capital control

and fixing the currency is adopted. This action shows that by forecasting the effect

of

a policy then unnecessary policy could be prevented from being implemented.

As for individuals, forecasting GDP could expose to the individual the trend

of

the GDP which could represent how well and how the country income is doing. An

early sign

of

a decreasing trend could signal an individual to be more careful in their

investment or some other activities that they are doing. For example,

if

each

individual has been exposed to the GDP forecast publication then they would know

that Malaysia is not doing so well between 1997 and 1998 thus can tell them to sell

their stocks so that they will not encounter high losses during the financial crisis.

1 2 Background o Study

1 2 1 History and Governance

o

Malaysia

Before the Malay Peninsula gained its independence in 1957, Malaysia was

initially ruled by Great Britain in the late

th

and

9

th

centuries. Later, Japan began

to occupy Malaysia from 1942 to 1945. Malaysia S first prime minister, Tunku

Abdul Rahman Putra (1957-1970) has brought Malaysia from colonialism to

5


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independence and he also proposed the idea

o

Malaysia. Malaysia was officially

formed in

963

when the British colonies in Singapore and the East Malaysian states

o

Sabah and Sarawak joined the Federation. In the beginning

o

the several years,

Malaysia was marred by a communist insurgency, confrontation with Indonesia, The

Philippines claim to Sabah and Singapore withdrawal in 1965. Malaysia s second

prime minister, Tun Abdul Razak Bin Dato Hussein (1970-1976) launched New

Economic Policy (NEP) in 97 which consists o two basic goals which are to

eradicate poverty and eradicate identification

o

economic function with race.

Malaysia third prime minister, Tun Hussein Onn (1976-1981) stressed on the

issue o unity through policies aimed at rectifying economic imbalances between

communities which result in the launching o National Unit Trust Scheme in 1981.

He also took a serious consideration in the concept o Rukun Tetangga and against

drug. Malaysia s fourth prime minister, Tun Dr. Mahathir bin Mohamad (1981

2003) also had successful diversified Malaysia economy from dependence on

exports o raw materials to expansion in manufacturing, services and tourism during

his reigns (Central Intelligence Agency (CIA, 2010).

The fifth prime minister, Tun Abdullah Ahmad Badawi (2003-2009) tried to

move the economy up the value chain by trying to attract investor in high technology

industries and also in pharmaceuticals. The current Prime Minister, Najib Razak

(2009-present) then continues the hard work

o

Tun Abdullah Ahmad Badawi. He

tried to boost the domestic demand and lower the dependencies toward export.

However, export remains significant especially on oil and gas. New Economic

6


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Model NEM) was also launched in 2010 to encourage more entrepreneurs to do

business in Malaysia CIA, 2010).

Malaysia is adopting constitutional monarchy which nominally headed by the

Yang di-Pertuan Agong or also could

e

regarded as the king. Each sultan among the

nine peninsular states would take turns to be the king and each elected king has 5

year term. The king is also the leader o the Islamic faith in Malaysia. On the other

hand, the executive power is vested in the cabinet that normally led by the prime

minister.

Apart from that, the legislative power in Malaysia is divided into two which

are the federal and state legislatures. In addition, Malaysia legal system is based on

English common law. Federal court reviews decision made by court o appeal.

Besides that, Peninsular Malaysia and East Malaysia each has their own high court.

In Malaysia, the federal government has authority in all matters for example external

affairs, federal citizenship, defence, finance, internal security, commerce and others

except for civil law cases among Malays or other Muslims which is under the

Islamic Law

U.S. Department

o

State, 2010).

1 2 2 Geography

Malaysia is located in South-eastern Asia where the peninsula is bordering

Thailand while one-third o the island in Borneo is bordering Indonesia, Brunei, the

South China Sea and the South

o

Vietnam CIA, 2010). This detail is shown in the

map below.

7


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Figure

:

Map o Malaysia

Source: Central intelligence Agency (CIA), 2010

The map above shows that the area in a lighter colour

o

brown is Malaysia.

Malaysia consists

o

two parts which are Peninsular Malaysia and East Malaysia.

East Malaysia consists

o

Sabah and Sarawak while the other states

in

Malaysia are

in

Peninsular Malaysia. The two parts o Malaysia

is

divided by South China Sea.

1 2 3 Economy

Malaysia is a high middle income country that had encounter transformation

from just being a producer o raw material into an emerging multi-sector economy

since 1970. The contri but ion

o

the other four former prime minister o Malaysia was

already stated previously where it had clearly explained that Tun Dr. Mahathir bin

Mohamad (1981-2003) had contributed highly to Malaysia economy.


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-

In the recent years, the fifth prime minister, Tun Abdullah Ahmad Badawi

2003-2009) t r i ~ to move the economy up the value chain by trying to attract

investor in high technology industries and also in pharmaceuticals. The hard work

of

the former Prime Minister was then continued by the current Prime Minister, Najib

Razak 2009-present). He tried to boost the domestic demand and lower the

dependencies toward export. However, export remains significant especially on oil

and gas. In order to encourage more entrepreneurs to do business in Malaysia, the

New Economic Model NEM) was launched in 2010 where its main motive is to

attract foreign direct investment then under NEM there is the Tenth Malaysia Plan

which outlines new reforms CIA, 2010). Figure 2 below show the trend in the Gross

Domestic Product GOP) of Malaysia:

Figure

:

Trend

of

GDP in Malaysia from 1980-2009

800000 000

700000 000

600000 000

c

500000 000

E

:: :

400000 000

a:

-

300000 000

0

200000 000

100000 000

0.000

i i i i i i

Source: International Financial Statistic IFS), IMF, Various Issues


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In overall, the Gross Domestic Product (GDP)

o

Malaysia had increased in

value from RM 53308 million in 1980 until RM 679687 million in 2009. From

Figure

2

The GDP o Malaysia increases only about

8.1

from 1980 to

1981

and

also only about 8.6 from 1981 to 1982 compare to the increase o 12.6 from

1982 to 1983 and 12.9 from 1983 to 1984 which clearly show that from 1980 to

1982 the growth o GDP was lower. This is because Malaysia experienced high

prices in 1980 and 1981 that were due to external factors. Oil prices increase from 47

percent during 1979 to 66 percent in

1981

and simultaneously the prices

o

industrial

raw materials also increased rapidly. The increase price o oil by Organization o the

Petroleum Exporting Countries (OPEC) causes powerful pressure on the consumer

prices that was only affected Malaysia in the latter part o the years (Cheng and Tan,

2002).

The GDP o Malaysia slumped in 1985 where it decreased about 2.6

compared to 1984 and decreased further about 7.6 in 1985. This is mainly because

o the international economic recession during the early 1980s. Because o the

moderate increase in demand and also the tight liquidity position, the capacity o

plants and labour forces are not utilize and as a result prices in 1985 increased at a

slower rate. Inflation rate in Malaysia decelerates and Consumer Price Index (CPI) is

less than 1 percent from 1985 to 1987. This marks a weaker demand condition and

also as a result

o

the world economic recession, exports and private sector income

depressed as a whole (Cheng and Tan, 2002).

However in 1990s Malaysia economy recovered and GDP began to grow

rapidly where it increased from RM 119081 million in 1990 to RM 300764 million


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in 1999 where in total GDP increased by 152.6 from 1990 to 1999 which is better

than the growth in the 1980s. The main reason that contributes to the rapid increase

o GDP during this period is the expanding o industrial sector especially in the

manufacturing and services sector (Encyclopedia

o the Nations, 2010). Besides that,

New Development Policy (NDP) was also introduced in 99 which emphasized that

the government would only help Bumiputera with potential and commitment. Other

than that, there was also heavy expenditure on infrastructure as an example the

building

o

the Twin Tower. The volume

o

manufactured exports especially

electronic goods and components also increase rapidly (Drabble, 2010).

However in 1997 to 1998, the GDP o Malaysia only has a slight growth which

only about 0.5 as a result o the Asian Financial Crisis that originated in heavy

international currency speCUlation that leads

to

major slumps on the exchange rate.

This crisis begin with Thai Bath in 1997 and in the end it began to spread rapidly

throughout East and Southeast Asia and affecting the banking and finance sectors.

This event causes a heavy outflow

o foreign capital and to counter this particular

situation, Malaysia government pegged its Ringgit at RM3.80 to the US dollar.

Because

o

this event, every country tried to spend as less as possible thus resulting

in the decrease

o

export in Malaysia (Drabble, 2010).

After the slight increase in GDP because o the Asian financial crisis, the

policy taken by Malaysian government manage to bring Malaysia out o the financial

crisis and GDP begin to grow rapidly again but later from 2000 to 2001, GDP again

begin to drop about 1.1 in 2001 compare to 2001. The decrease in GDP from 2000

to 2001 is as a result o the global economic downturn and the slump in information


26/128

technology (IT) sector. As Malaysia growth was almost driven by exports especially

electronics and because o the global economic downturn, Malaysia experienced a

contraction in exports which eventually lead to the decrease in GDP (Malaysia

Canada Business Council, n.d.).

After that, GDP

o

Malaysia again begins to increase rapidly until 2009 where

there is a decrease in GDP about 7.9% compared to 2008. This decrease is due to the

unexpected drop

o

the service sector and the drop

o

the service sector is primarily

influence by the sub-sectors linked to the manufacturing sector. During the first

quarter o 2009, there is a decline in electricity, gas, water and transports and storage

services. All other sectors also show a decline except for construction (Amanah

Mutual Berhad (AMB), 2009).

1 3 Motivation o Study

The annual growth in the GDP o Malaysia is increasing rapidly especially in

the 1990s but suddenly in 2009 the growth in Malaysia GDP suddenly decreases

which is rather astonishing because the GDP during the financial crisis although just

increases it value by a small percentage but it did not decrease as in 2009. Thus, this

study attempts to identify the factors that influence the GDP and the dynamic

relationship between these factors and the country GDP.

Towards the end after identifying the appropriate factors, this study then also

attempts to forecast the GDP in the context

o

Malaysia in order to analyze the trend

o

GDP in the future years. Forecasting is an important tool for the economy today.

2


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Forecasting is useful for evaluating the future trend

o

the economy. Furthennore,

forecast GDP can help to anchor the expectation

o

the finns and households.

According to Weber (2009), forecasting play an important role in introducing

monetary policy and

o

course differences in strategy mean forecast also play a

different role. Thus, the growing importance

o

forecasting to the government

stimulates the passion to build a model that could accurately predict the future

movement

o

GDP.

1 4 Problem Statement

According to Weber (2009) in a conference organised by Deutsche

Bundesbank, Freie Universitat Berlin and the Viessmann European Research Centre,

he states that the reason central bank have strong interest in forecasting is because

o

the substantial and variable lags in monetary policy transmission mechanism as

central banks could not influence current inflation and output. Thus, monetary policy

should be more forward-looking and take a medium-tenn perspective. As a result

from this, forecasts for inflation, output and other macroeconomic variables are

essential input in the monetary policy decision-making process.

The interest in, and demand for, macroeconomic analyses at

high

frequencies

especially forecast has increases in the recent years including Malaysia as a lot o

commercial banks forecast the country GDP in order to have an idea to Malaysia

future outlook to enable them to charge the appropriate amount

o

interest and also

introduce new policy that related to banking.

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However, conducting analyses and forecasting is not an easy task as different

forecasting models yield different results. Caraiani 2008) forecast the Romanian

GDP using a small Dynamic Stochastic General Equilibrium DSGE) model while

Angelini, Banbura Runstler 2008) estimate and forecast monthly national

accounts for Euro Area using a dynamic factor model. Moreover, Debenedictis,

1997) does a study in British Columbia by constructing a small autoregressive

AR) model.

Kogid, Mulok, Lim 2010) used consumption expenditure, exchange rate

and foreign direct investment as the determinant of economy growth in Malaysia. On

the other hand, Anaman 2004) used government size measured as a ratio of total

government expenditures, total investment and annual growth of labour as the

determinant in Brunei. Kogid, Mulok, Lim 2010) found that government

expenditure have a role in economic growth but only as a catalyst while Anaman

2004) found that government size measured as a ratio

of

total government

expenditures could highly impact the economic growth depending on the size.

Moreover, according to Keynes 1936), government expenditure is one of the

determinants

of

income.

Besides, Kogid, Mulok, Lim 2010) also found that similar to government

expenditure, exchange rate also did not play an important role as a determinant for

Malaysia but according to Chong Tan 2008) the role of exchange rate is still

prevailing especially in the long run for small and open economies. Other than that,

there also exist an enormous theoretical literature on the temporal behaviour of

income and output spanning such areas in public finance, monetary economies,

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internationaleconomiesanddevelopmenteconomies(Ansari,2002). Inotherwords,

different countrieshavedifferentdeterminantsof GDP.Thus,thisstudyintheend

attempttosolve:

1. WhatarethedeterminantsthatinfluencetheGDPofMalaysia?

11. WhichforecastingmodelscanprovideasatisfactoryaccuracyfortheGDP of

Malaysia?

1.5 Objective of

Study

1.5.1

General

Objective

Theaimof thisstudyistofindthedeterminants of GDPofMalaysiaand use

themtoforecasttheGDP

of

Malaysia.

1.5.2 Specific Objective

Thespecificobjectives

of

thestudyare:

1. ToexaminethedeterminantsofGDP inMalaysia.

11.

ToforecasttheGDP

of

Malaysia.

HI

Toevaluatetheforecastingaccuracy

of

theforecasts.

1.6 Significance

o

Study

Forecasting the GDP

of

Malaysiacould generate future valuesonGDP and

thesefuturesvaluescouldbeusedtodeterminethetrendandbehaviour

of

theGDP.

Thebenefitof

beingabletodeterminethetrendofGDP isthegovernmentwouldbe

abletoknowtheconditionof theGDPforthefollowingquarters

or

evenyears.This

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advantage enables the government to find policies that match the condition of the

economy for the following quarters

or

years. Thus, forecasting the GDP is important

as this could be a tool to help the government to evaluate the future economy and

find out whether the new policy and new approaches that was about to be introduce

suits the economy.

Besides that, accurately forecast the future trend

of

the GDP also could alert

the government

if

there is going to be a slump in the country GDP. Then, the

government could implement plan to counter the slumps earlier to avoid the country

suffering from the economic downturn. On the other hand, the future trend

of

the

economy also can help the central bank introduce proper monetary policy especially

when there is a decreasing trend detected in the future value

of

the GDP.

Indirectly, this study is also important to the public as when government and

the central bank take the appropriate measures according to the future trend

of

the

GDP, the public will be benefited as the government would always find the most

beneficial method that would lead the country and the people better off. Thus, the

public would be spare from any economy slow down and would be able to prosper

when the economy is booming.

In overall, forecasting the GDP is important for the government especially

when new policies is intended to be introduced and to prepare for any economic

downturn

in

the near future.

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1.7 tructure o the study

This paper is organized into five chapters in which Chapter One will

briefly discuss on the background o Malaysia then later throughout Chapter One it

will deals with problem statement the objectives motivation o study significance

o study and also the scope o study. Then Chapter two contain literature review o

the theoretical studies o forecasting GDP and also review on previous empirical

studies. Then Chapter three will describe on the research methodology used in the

study. Later Chapter four explains and report the result o the empirical analysis and

lastly Chapter Five concludes the study conducted along with some policy

recommendation.

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Chapter Two

Literature Reviews

2 0 Introduction

Gross domestic product (GDP) is the basic measurement o a country

economy s performance. GDP is a measure o the value o all the goods and services

newly produced in a country during some period o time (Taylor, 2007). GDP can be

defined in three ways but in concept it has the same meaning. First, GDP is equal to

the total expenditures o all final goods in a given time. Second, GDP is the sum o

all the stages o production value added, by all the industries within a country. Third,

GDP is the sum

o

the income generated by the production in the country in the

period.

Time series forecasts are used in all kinds o economic activities which include

setting

o

monetary and fiscal policies, state and local budgeting, financial

management and financial engineering. The key element that must present in

economic forecasting is selecting the forecasting model and also assessing and

communicating with the uncertainty associated with a forecast, and guarding against

model instability. One o the types o economic forecast is forecasting GDP and its

determinants. The determinants used to forecast GDP is mostly national account data

which include export, import, real effective exchange rate and more. Besides that,

industrial production index is also used as a determinant basically because industries

are one

o

the sectors that contribute to a country GDP. However, the significance

o

the determinant towards GDP varies according to country, state and region. Some

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studies that was done also show that there are cases when money could also affect

the GDP or in other words money is also used as a determinant o GDPJ.

In order to understand more on the result found by different studies, this

literature review is divided into 5 sections which consists o 2.0 which is the

introduction

on

generally what does GDP and forecasting means. While, 2.1 is the

theoretical framework on how the determinants

o

GDP would lead to the changes in

GDP, 2.2 encompasses the empirical testing procedure that would be separated into

two parts which the first part would discuss on the empirical model used to forecast

the GDP and the second part is on the method used to forecast. Then, 2.3 consist o

the empirical evidence or finding o previous study lastly 2.4 is the concluding

remarks.

2 1 Theoretical framework

According to the previous studies, some o the determinants o economy

growth mentioned are:

2 1 1 Stock Market

Stock market return is one o the determinants o Gross Domestic Product

(GDP). Stock market return is often represented by stock price or share price. Stock

price is negatively correlated with interest rate. Changes in interest rate could affect

stock price through substitution effect. This is mainly because that higher interest

I See Pigou (1943) and Patinkin (1965) which the relation o real balance effect originated.

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rate would result in contractionary monetary policy that would cause the return

o

stock to become lower. As interest rate goes higher fixed income securities became

more attractive than holding stock in other words people would prefer to save their

money to earn a higher return than to loan money and suffer from the high interest

when they need to pay back the loan BaneIjee & Adhikary, 2009). As loan

decreases, economic activities that involve transaction and investment will

eventually decrease and in the end would lower the output o the country. The lower

the stock price indicates the lower the investment as investment is lower than the

output is also affected as investment contribute to the country GDP. This conclude

that stock market and GDP is positively correlated.

2 1 2 Real Activity

Another determinant

o

GDP is changes in real activity which is often

represented by industrial production. Changes in real activity is related to interest

rate as a decrease in interest rate would cause an increase in investment and therefore

in the future production. As discuss above, interest rate could also affect stock price

now through variation in future product Peiro, 1996). The relationship between

stock price and industrial production had been proven by Fama 1981, 1990) that

shows the close relation between real returns and growth rate in industrial production

empirically using annual data. Indirectly, changes in industrial production would

affect the stock prices and also the interest rate that eventually would lead to the

changes

o

the country output. Thus, as real activity in the country increases, the out

or GDP will eventually increase. This shows a positive relationship between real

activity and GDP.

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2 1 3 Money Supply

Money supply is positively correlated with GDP which is illustrated by Arnold

(2008). He state that a change in the aggregate demand and thereby change the price

level and also GDP in the short run. This is to say that an increase in the money

supply would shift the demand curve to the right which will move the economy to a

higher point. On the other hand,

i

money supply is decrease then lower level

o

GDP will be produce

2 1 4 Exchange Rate

Apart from that, exchange rate is another GDP determinant. According to Yau

and Nieh (2009), there are two theories that are about the relationship between

exchange rates and stock prices which is the traditional and portfolio approach.

Traditional approach state that depreciation in the domestic currency would cause

the local firm to become more competitive that lead to an increase in their exports

and would yield higher stock price. This approach implies that exchange rate and

stock price is positively related. Meanwhile, the portfolio approach states that an

increase in the stock prices would induce the investor to demand more foreign assets

which in the end could cause an appreciation in the domestic currency. This

approach on the other hand implies that exchange rate and stock price are negatively

related.

Empirically, there are a number

o

researchers that proven that a significant

relationship exist between the exchange rate and stock prices. Mok (1993) found a

weak bi-directional causality between stock prices and the exchange rate. Nieh and

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Lee 2001) also found bi-directional causality between stock prices and exchange

rates but only in the short run. Besides them, there are a few other researchers that

either found a weak association or a zero association between stock prices and

exchange rate Franck and Young, 1972; Bartov and Bodnar, 1994; Fernandez,

2006). The changes in exchange rate would eventually affect the stock prices. Stock

prices

on

the other hand could influence the changes in GDP as stock price reflect

investment. The lower the stock price indicates the lower the investment as

investment is lower than the output is also affected as investment contribute to the

country GDP.

n

the other hand, overvalued exchange rates are associated with shortages of

foreign currency thus will damage the economic growth. In other words, an increase

in the undervaluation will boost economic growth as well as a decrease n

overvaluation. This indicates that exchange rate and economic growth are negatively

correlated.

2 1 5 Interest Rate

Interest rate is also one

of

the most important determinants in GDP. As had

shown from all the above determinants, almost all of the determinants have the

present

of

interest rate as a transition mechanism. As interest rate increases, holding

stock would become unattractive as they cannot gain much as a result of the high

interest rate which causes them to have to pay a lot if they loan a lot. This

phenomenon induced people to save rather than invest. Saving generally is a kind

of

withdrawal which could decrease the amount of output produce. The relationship

between interest rate and GDP was portrayed by Obamuyi 2009) that imply the

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behaviour o interest rate is important in economy growth that normally represented

by GDP or GDP growth as interest rate could affect investment and investment could

affect the output

o

the country.

2 1 6 Trade

In addition, trade is also a determinant o GDP and this sense trade constitute

o export and import. According to the Keynes 1936), export and import is a factor

that could influence the amount o the income o the country. This is because when

the demand o export for other foreign countries increases thus directly it could

increase the income o the country whereas indirectly is through the changes o the

term o trade. Exports also would likely to alleviate foreign exchange constraint thus

provide greater access to international market. In other words, as more goods and

services were bought by other countries, the goods and services would be paid by

them thus when they pay for its then the income

o

our country would eventually

increase. Other study that emphasizes the importance o exports towards the country

output

or

economy growth is Dritsaki, Dritsaki & Adamopoulos 2004) which found

that economic growth, trade and FDI appear to be mutually reinforcing. Economy

growth used in their study is GDP. Other studies that support the significance o

export to GDP are Sentsho 2000), Fugazza 2004) and Awokuse 2002).

2 1 7 Consumption Expenditure

According to Kogid, Mulok & Lim 2010), consumption expenditure is one

o

the variables that play an important role as a determinant factor to a country

economic growth in this sense GDP in Malaysia. Besides that, Keynes 1936) also

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discovered that consumption expenditure is one o the determinants for the income

o a country. As the consumption expenditure increases, this means that more goods

and services are being consume and injecting more income or revenue to the market

thus increasing the revenue o the country. There are also a number

o

studies done

regarding the relationship between consumption expenditure and economy growth

such as FoIster and Henrekson (1999) and Kweka and Morrissey (1998) although

both o these studies reported no evidence o relationship between economy growth

and consumption expenditure for their respective country.

2 2 Empirical Testing Procedures

2 2 1 Specification

o

models

Some methods that were used by previous studies to model the determinant o

GDP are discussed by Anaman (2004) that assume a Cobb-Douglas functional form

and restate the economy wide production function as:

G

=

Boexp

1

G

2

B

Z

G

3

B

) TEXPORT)B

4

TLABOR)Bs X

t

34

(2.1)

where exp denotes the exponential operators, G refers to government size where it is

defined as government expenditure divided by GDP, TEXPORT is the total annual

level o exports, TLABOR is the total annual level o labour inputs, TCAPITAL is the

total annual stock o capital inputs, ASIANFC s the dummy variable with a value o

1 for the years. By taking logarithm in equation (2.1), the empirical model used in

this study is as follows:

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GROWTH

t

= o +

B

1

GOVSIZ)t

+

B

z

GOVSIZ2)t

+

B

3

GOVSIZ3)t

B

4

GTEXPORT)t + Bs GTLABOR)t + B

6

JNVGDP)t + B

7

ASIANFC)t + U

t

2.2)

where

GROWTH

is the annual growth

o

real gross domestic product, GT XPORT is

the annual growth rate o the real value o total exports, GTLABOR is the annual

growth rate o total labour force, and GTCAPITAL is the annual growth rate o the

real value o total capital stock. GOVSIZ is the relative size o government defined as

the ratio

o

government expenditures to gross domestic product. GOVSIZ2 is the

square o

GOVSIZ, GOV5)JZ3 is GOVSIZ raised to the third power and INVGDP is

the lagged ratio o total investment to gross domestic product, and

U

is the error term

that is assumes to be normally distributed.

t is hypothesised that government size will impact the economy growth in a

cubic function. Initially small relative size o government hampers economic growth

while medium-sized government accelerates economic growth through the provision

o basic infrastructure and improved legal framework, and an increased growth o

total exports, labour and investment inputs are hypothesised to lead to increase

economic growth. The growth on human-made capital inputs on the other hand is

expressed in the form o a ratio o investment to GDP. Investment represents change

in total capital stock and should be divided by total capital stock to derive growth

rate

o

capital. So, small relative size o government constitutes a negative sign while

medium-sized government, exports, labour and investment is positively correlated

with economic growth.

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Gounder (1999) also uses the same method as Anaman (2004) that study the

effect o military coups on Fiji economic growth for the period 1968 to 1996 which

the variables used in the study are annual growth rate o national income, annual

growth rate

o

labour force, total investment to output ratio, private investment to

output ratio, government investment to output ratio, annual growth rate o exports,

political instability variable o military coups.

Besides that, Sentsho (2000) uses a similar method as the previous authors to

assess whether export revenues derived from an enclave sector like the case

o

mining in Botswana can lead to significant and positive economic growth in a

country. The variables used in the model are real GDP, ratio

o

gross domestic

investment to GDP and labour force. The unconventional inputs used in this study

include aggregate export, primary export, manufactured (non-traditional) export,

imports, private sector consumption in real GDP, government sector, previous period

growth in real GOP and world GOP. The sample period is from 1975 to 1997 and to

capture the economic boom that came with the opening

o

the Jwaneng diamond

mine and the construction o its town a dummy variable is used.

On the other hand, Kogid, Mulok, Lim (2010) begin the functional exact

relationship between the dependent variable and independent in logarithmic form (L)

where Yt is a function o it which can be specified as below:

(2.3)

where Yt is LGDP at time

t it

is Log Consumption Expenditure (LCE), Log

Government Expenditure (LGE), Log Export (LX), Log Exchange Rate (LER) and

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Log Foreign Direct Investment (LFDI) at time t, =

1

2, 3, ... , n. Thus, to allow for

the inexact relationship between economic variables, the deterministic economic

growth function is modified as follows:

(2.4)

where is known as disturbance or error. The disturbance term may well represent

all those factors that affect economic growth but not taken account explicitly. FDI

contributes largely to the development o East Asian economy and from his

literature, real GDP was found to have a positive impact to FDI inflow. Export on

the other hand is the most research determinant as one

o

the reason because most

developing countries practice export promotion. Export is positively correlated with

income as exports could increase the income o a country. Expenditure and exchange

rate also constitute a positive relationship. However, according to them theory itself

is not enough as theory provide little evidence. Besides that, Chen Feng (1996)

also uses the same model using average growth rate in real GDP per capita, political

variables to capture regime instability, political polarization, and government

repression and control variables to measure economic conditions.

2.2.2 Forecasting Models

There are a few models that mostly used by researchers to forecast the GDP.

Some o these models are shown in the next subsection:

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2.2.2.1 Vector Autoregressive V AR) Model

Saraogi (2008) did a research to forecast the quarterly growth rates

in

the GOP

of

Australia using a V

R

model, which has some obvious benefits over a pure

simultaneous equation system. In this model, some variables are treated as

endogenous and some as exogenous. This model is based on Sims

2

The model as

research by Saraogi (2008) is:

Y

t

=

a

+

P l

t

_

i

+ yP l

t

i

+

T l

t

-

i

+ 8ESI

t

_

i

+

Ct ,

(2.5)

where,

Y

t

Quarterly growth rate in GOP at constant 2000 US

a

Intercept tenn

l

t

Human Capital Index with

i

th

time period lag

P l

t

i

Physical Capital Index with i

th

time period lag

l

t

- =

Banking Index with i

th

time period lag

ESl

t

-

= External Sector Index with i

th

time period lag

P

y, TJ 8 Regression Coefficients

Ct Error tenn

2 Sims ( 1980) states that if there is simultaneity exist among the variables then they should be treated on an equal

footing where there should be no distinction between the endogenous and exogenous variables.

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Reimers and Seitz 2003) assess the predictive content of MI using different

types of V AR models. The first model use is unrestricted V ARs as this model is a

good empirical representation

of

economic time series as long as lags are included

3

Their model is:

2.6)

where

X

t

is the vector endogenous variable,

MI r

the matrix deterministic terms,

especially the intercept term and linear deterministic trend, Al to

o

are the

symmetric coefficient matrices and 0 is the selected lag order. However

if the

variables are cointegrated but not stationary, a Vector Error Correction VEC) is

used. VEC becomes a V AR model in the first differences.

Then from a study by Barhoumi et aL 2008), they use a quarterly type ofV AR

model to forecast. They run bivariate V ARs including GDP and the quarterly

aggregate of a single monthly indicator and later they average the forecasts across

indicators. Their model is:

Z

Q -

+

,, ,Pi A Z

+

Q

i

=

1, ... , k , (2.7)

i t

- fl i ' s=1 s i.t-s e i . t

produced. Then Pi represent the lag length.

3 See Canova 1995) on V AR specification, estimation, testing and forecasting.

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2.2.2.2

Dynamic Stochastic General Equilibrium DSGE) model

Caraiani (2008) study the Romanian GDP using a small DSGE modeL The

model consists

of

a finite number

of

representative agents characterized by an

infinite life. Each agent will maximize the expected lifetime utility. Consumption,

investments and labour effort will be optimally chosen under the constraints given

by its income. Based on the model studied by Caraiani (2008), the model is given by:

t C

t

1-11

1

]

maxEo

[

Lt=of3

-

ANt

I

(2.8)

1-1')

where

f

is the discount factor,

t

is the consumption, is the relative aversion

coefficient,

t

is the number

of

hours worked and A

is

the parameter that symbolizes

the utility function.

2.2.2.3 Dynamic Factor Model

The concept

of

this model is based on the assumption that macroeconomic

variables are better described with small unobserved common factors. Krajewski

(2009) did a study using dynamic factor model that is based from Stock and Watson

(1998) where he let

Yt

stand for a variable and

X

t

express the vector of N variables

that contain useful information to forecast Yt. In the model all variables Xit which

include output and sales, construction, domestic and foreign trade, prices and labour

market, budgetary and monetary policy that contain in vector X

t

may be expressed as

a linear combination

of

current and lagged unobserved factors

fit .

u

= AiCL ft

eit

I

for

i =

1,...,

N

(2.9)

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where t stands for vector f

o

unobserved common factors at moment t and Ai L

)

represent lag polynomials and eit express an idiosyncratic error for variable x In

the end, Yt may be noted as the function o current and lagged common factors

contained in vector t and the past values o variable Yt with the following formula:

Yt =

P(L) t +

y(L)Yt

+ et

(2.10)

Model described in equation (2.10) and (2.11) are both dynamic factor model.

Besides Krajewski (2009), a study on forecasting the GDP o Austrian is also

done by Schneider and Spitzer (2004) using this model with the only difference is

that now the model is generalized. Their model is written as:

(2.11 )

where X

it

is called the common component and include variables such as national

account data, WIFO quarterly survey, monthly survey data, prices, foreign trade,

labour market, financial variables and industrial production. On the other hand

Cit

is

the idiosyncratic component. In addition, bi L) is a vector o lag polynomials and

lastly

J1.t

is a q-dimensional vector o common stocks. In their study, the q-

dimensional process is assumed to be mutually orthonormal white noise with unit

variance Cit on the other hand is orthogonal to J1.t-kfor any

k

and i.

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A research by

D

Agostino, McQuinn and O'Brien (2008) was also done by

using the same model and applying it to now cast the GDP

of

Irish. Their model is

based on the model

of

Giannone, Reichlin and Small (2007)4. The model is:

(2.12)

(2.13)

(2.14)

where in equation (2.l3) ,

t

include economic activity, price dynamics, business and

consumer sentiments surveys and financial indicators and t

is the sum

of

two

orthogonal components, the common component

x

t

and the idiosyncratic

component The common component is the product

of

a n x matrix

of

loadings

A

and x 1 vector

of

latent factors f .Meanwhile, the idiosyncratic component is a

multivariate white noise with diagonal covariance matrix

L{

On the other hand,

factor dynamics are described in equation (2.14) which is a VAR (p).

2.2.2.4

Univariate Autoregressive Integrated Moving Average ARIMA)

A research by Hoehm, Gruben and Fomby (1984) was done to forecast the

economy

of

Texas in which one

of

the models used is an ARIMA model. This model

is selected as it treats each variable in isolation no matter in estimation

or

in

forecasting. The model that they come out with is:

4 See Giannone. Reichlin and Small (2007) about nowcasting the real time informational content of

macroeconomic data where they developed a formal method t evaluate the marginal impact that intm-monthly

data releases have

on

current-quarter forecast

of

real GDP growth. Their model is use

in

the study of Agostino.

McQuinn and O'Brien (2008)

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(2.15)

where L is the lag operator.

t

is the natural logarithm

of

the series, the variables in

the series include Texas Industrial Production, Consumer Price Index, Payroll

Employment, Household Employment, Texas Labour Force, Deflated Personal

Income and Deflated Retail Sales and at is a normally distributed unobservable

random variable with zero mean, finite and constant variance and have zero

autocorrelation at all lags. There are autoregressive term (lagged y s) and q

moving average terms (lagged a's).

2 2 3 Empirical Method

Before the variables are estimate, all the variables must be makes sure that they

are in the right order. Besides that, only cointegrated variables can be used to

forecast as this means that they exhibit long run relationship.

2 2 3 1

Stationary test

According to Lu (2009), a time series that is about to be analyzed should be

make sure that it is stationary before specifying a model. Lu (2009) had used KPSS

and ADF test to investigate whether the time series are stationary. ADF test is used

to verify whether the series is stationary while KPSS is to measure whether unit root

exist.

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Debenedictis (1997) also tested for unit roots. Like Lu (2009), she also adopt

ADF test but if the errors are not independent and seasonal data is detected then the

basic ADF

5

test needs to be modified

6

Besides that, Anaman (2004) like the two other researchers also conduct unit

root testing. He adopts DF or ADF, Phillips-Perron (PP), Kwiatkowski-Phillips

Schmidt-Shin (KPSS) and Ng-Perron (NP) test.

2.2.3.2 Johansen Multivariate ointegration Test

Anaman (2004) apply Johansen Multivariate Test to test the movement

of

the

variables in the long run. They only use the variables of the same integration level to

test the presence of cointegration level. The lag structure is determined automatically

by the statistical package. The core movement in the long run are as Johansen (1988)

and Johansen and Juselius (1990) and can be defined as follow:

(2.16)

Where,

i = 1 TIl

-

...

-

TID (i =

1 ...

, k - 1),

And

=

1

TIl -

...

- TID

5 The basic Augmented Dickey-Fuller test that was presented

by

Dickey and Fuller (1979, 1981)

6

A modified ADF test is based on Said and Dickey (1984) research.

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Model 1) is expressed as a traditional first difference of a V AR-model except

for

t

b

thus coefficient matrix

n

is used to be investigated to find out whether it

contains information about long-run relationship among the variables in the data.

The rank

of

n depends on two likelihood ratios which include Trace test and

Maximum Eigenvalue test. Trace test

as

expressed as:

2.17)

where T denotes the number of valid observations for estimation use and is the i

lb

largest estimated eigenvalue.

n

the other hand, Maximum Eigenvalue is expressed as:

2.18)

where T denotes the number of valid observations for estimation use and

1,. 1

is the

largest eigenvalue

at r 1.

After the variables in the equation are prove to

be

stationary and cointegrated

with each other then the next step is to forecast the mode1.

2 2 4 Forecast Criteria

Evaluating the forecast accuracy is also important as it will give an idea on

how

well the model could represent the real economy. Thus, a few test to determine

the accuracy of the model is conducted.

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2 2 4 1

Information riteria

On the other hand, according to Krajewski (2009), in practice usually the

number

of

factors necessary to represent the correlation among the variables is

usually unknown. Krajewski (2009) determine the number of factors empirically

using the information criteria suggested by Bai and Ng (2002) which is given as:

IC

i

k) = In V k)) = k N;;) In ::T))

(2.19)

IC

2

k) = in

V k))

=

k

N;TK) in ~ T

(2.20)

IC

2

k) = in

V k))

=

k

C : ~ ; T ,

(2.21)

where

V k)

is the residual sum

of

squares from k-factors model and

C

T

=

min{vNv T}. N which the number of factors yielded from the equation or test will

represent the correlation among the variables.

2 2 4 2

Root Mean Square Error (RMSE)

In the study

of

Guegan and Rakotomarolahy (2010), using five years

of

vintage data, they adopt RMSE to evaluate the accuracy of the forecast by computing

the RMSE for quarterly GDP flash estimates. The RMSE criterion used for their

study on the final GDP is:

(2.22)

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Where T is the number

of

quarters between the period

7

that their estimated and

Y

is

t

the Euro area flash estimate for quarter

t

Lower value of RMSE indicate a better

model.

In the study done by Andersson (2007), he also uses RMSE as one of his

method to measure the accuracy

of

the forecast. According to Andersson (2007),

RMSE is the most frequently used measure and is known to be more sensitive to

outliers than MAE8. However RMSE is the only one he uses to rank the performance

of

the model. After that, F test is use to check whether the differences in the forecast

ofRMSE

is significant. The F-test is formulated as:

H

Z

F - i=lel i

(2.23)

H z

i=Z eZi

The larger value

of

the forecast RMSE is put in the numerator. The null hypothesis is

for equal forecasting performance for the two models being compared. The intuition

is that the F-value will equal unity

if

the forecast RMSE from the two models are

equal, while a very large F-value implies that the forecast RMSE from the first

model is substantially larger than the forecast RMSE from the second model.

2 2 4 3

Mean bsolute Percentage Error MAPE)

Gupta (2006) also adopted MAPE in order to evaluate the forecast accuracy.

The statistic

of

the MAPE test adopts by Gupta (2006) can be defined as:

7

Time period define in their study is QI 2003 and

Q2

2007, T=18.

8 It is the averages

ofthe

absolute values of the out-of-sample forecast error.

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(

2: Labs

At+n-tFt+n)) X

1

(2.24)

N At n

where

abs

stands for the absolute value. For n 1 the summation runs from 2001: 1

to 2005:4; and for n = 2, the same covers the period of2001:2 to 2005:4 and so on.

Note that,

At n

denotes the actual value

of

a specific variable in period

n

and

t Ft n is the forecast made in period t for t n. Percentage errors are not scale-

independent thus they are usually used to compare performance across different data

sets. The higher percentage error indicates lower accuracy.

2.2.4.4

Encompassing tests

This is another method to access the relative performance

of

the models which

was study by Barhoumi et al. (2008). In this test, two alternative models 1 and 2 are

based

on

regression

of

the actual data

Yt

Q

on foreca')t ~ and

z ~ t

from two models.

The equation is:

(2.25)

and

where

It

gives the optimal weight

of

model 1 in the combined forecast. However, in

the extreme case, a value

of It =

1 indicates that

modell

dominates model

2.

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2 3 Empirical Evidence

Anaman (2004) conduct a study in Brunei Darussalam to find the country

determinant using yearly data from 1974 to 2002 obtain from the various issue

o

Brunei statistical yearbook, 1974 to 2000 for private sector labour force and 1971 to

2000 for government workforce. Multiply regression analysis based on relatively

new cointegration technique was used to construct a variant

o

the neoclassical

model. The results from his research show that, the growth o export significantly

influenced long-run economic growth rates and the other main factor is the relative

size o government where it influence the long run growth rate in the form o cubic

function. Large government sizes impeded economic growth while moderate

government sizes enhanced economic growth.

Kogid, Mulok, Lim and Mansur

201

0) conduct a study to determine the

economic growth factors in Malaysia using yearly data from 1970 to 2007. They use

cointegration analysis and causality approach and also ECM to analyze the

relationship between economic growth and the determinant factors. They find that

consumption expenditure, government expenditure, export, exchange rate, and

foreign direct investment cause economic growth in the short run. However,

individual test conducted point out that only consumption expenditure and export

cause economic growth. Thus, they conclude that consumption expenditure and

export play important role as determinant factors to economic growth.

Gupta (2006) conduct a study in South Africa to forecast the country economy

using quarterly data from the period o 1970: 1 to 2004:4 with an out o cast sample

from

2001:1 to 2005:4 with a Bayesian Vector Error Correction Model (BVECM)

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and find that BVECM in general except for long term interest rate measure, produces

the most accurate forecasts relative to the alternative models s it is best suited for

forecasting South African GDP. The Mean Absolute Percentage Error (MAPE)

values from the VECM are less than those generated from V AR model. The VECM

and BVECM correctly predict the direction

of

change for the entire variable.

This finding is later supported by

Uu

Gupta Schaling (2010) that also

forecast South Africa economy using quarterly data from the period of 1970: 1 to

2000:4 and with an out of sample forecast from 2001:1 to 2005:4. They estimate the

model with the maximum likelihood technique on real gross national product (GNP)

then based on a recursive estimation using the Kalman filter algorithm from the

model are then compared with the out of sample forecast from classical and

Bayesian variants of the VAR. They say that in general, the estimated hybrid DSGE

model outperforms the classical V AR, but not the Bayesian V ARs. The Root Mean

Square Error (RMSE) generated from the BVAR is much smaller from both the

hybrid model and unrestricted V AR.

However in the earlier studies, Debenedictis (1997) does a study in British

Columbia where a small autoregressive (V AR) model is construct using quarterly

data from the period of 1961: 1 to 1991:4 and an out of cast sample from 1992: 1 to

1995:4 and find that V AR yields the least biased forecasts for employment and

British Columbia GDP while ARlMA yields the least biased estimates for Canada

GDP and prices. The result indicates that 98 and 89

of

the actual data fell within

the 95 confidence intervals for ARIMA and VAR models respectively. Thus, VAR

is a promising forecasting tool in the case ofBritish Columbia.

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Schneider Spitzer (2004) foreca')t the GDP o Austrian using a mixture

o

quarterly and monthly data from the period

o

1988: 1 to 2003 :2. They use a

generalized dynamic factor model to produce the short term forecast and the model

follows the frequency domain approach. They state that broad movements o the

business cycle are predicted correctly as the results from Pesaran-Timmermann test

show that the change in the GDP growth rates direction is correctly predicted. On the

other hand, the forecasting error for two steps ahead forecast is smaller than one step

forecast. In conclusion, Factor model perform better in small data set, the number

o

dynamic factor could impacts heavily on forecast performance.

In addition, from the result obtain by Caraiani (2008) where he forecast the

Romanian GDP using a small DSGE model using quarterly data from 2006: 1 to

2007:2. The model is forecast based on the posterior distribution o the model

parameters as a result from the Bayesian estimation. He suggests that DSGE model

can have a good performance for the trend o the growth however its overall

performance depends on the level

o

temporary productivity shocks. The projection

did by him show that a growth rate o almost 6 which is in line with other

estimates. Thus, DSGE model also could be used to forecast the GDP

o

Romania.

Angelini, Banbura Runstler (2008) estimate and forecast monthly national

accounts for Euro Area using quarterly data from 2000: 1 to 2006:2 and also using

monthly data for a different kind o method from 1998: 1 to 2006:6. The growth in

euro area monthly GDP and its components was estimate and forecast using a

dynamic factor model then the model

s

extend to integrate interpolation and

forecasting together with cross-equation accounting identities. They find that

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forecasts and monthly estimates are consistent which will be advantageous for

monitoring economic development in real time. For GOP and a number o

components, the model beats forecasts from time series model based on quarterly

data and from forecast average from bridge equation. As regards to GOP, AR (1),

V AR and bridge equation improved upon naive forecast where the gains in RMSE

are close to 20 .

Mittnik Zadrozny (2004) forecast the quarterly GOP at monthly intervals

using monthly IFO business condition data in Germany. They use quarterly data

from 1970: 1 to 2003:4 and monthly data from 1970: 1 to 2003: 2 to evaluate the

GOP forecast. They use Kalman filtering method that based on maximum likelihood

estimation method to forecast. They state that monthly GOP forecast are feasible and

produce better short term GOP forecast. Meanwhile, Quarterly data produce better

GOP forecast and aggregation affect the forecast. IFO variables improve long term

GOP forecast and to estimate larger models we must extent to mixed-frequency

forecasting. Choosing 0 options and unit option in analogous test improve accuracy

o

GOP forecast.

On the other hand, Barhoumi et al. (2008) predict the growth rate

o

quarterly

French GOP from 1988:3 to 2006:4. The model is designed to integrate the monthly

economic information through bridge models for both demand and supply sides

o

GOP. However different variables have different starting period depending on the

availability

o

the data. They find from the result they obtain from the euro area

countries that exploiting the timely monthly release fare better than quarterly models.

Besides that, a finding by Golinelli and Parigi (2008) suggest that the relevance o

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indicators tends to varnish at longer forecasting horizons however, the forecast of the

first GDP release obtained from real-time data and from the latest available data do

not differ significantly.

Hoehn, Gruben Fomby (1984) forecast the economy

of

Texas with some

time series method using quarterly data from 1969 to 1980 to forecast and from

98 : 1 to 1983:2 evaluate the performance using a V AR model and they find that

mean errors for all variables except labour force generate by two alternative ARIMA

models were negative. Besides that,

RMSE also generally exceeded the standard

error of the within sample often quite substantially. Among the seven series studies,

they state that the two employment series seem the most important for the regional

forecaster to watch.

2 4 Concluding Remarks

As a conclusion, chapter has reviewed literatures that are taken from previous

study which is related to forecasting and now casting the GOP. From previous

researches, it is known that there is a lot

of

model used to predict the GDP

of

a

country. Th

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