VECM Model and Modeling International Tourism Research_ IMF

8/14/2019 VECM Model and Modeling International Tourism Research_ IMF

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VECM Model and Modeling International

Tourism Demand in Thailand

(Working paper No. 4/2006)

Chukiat Chaiboonsri

Received a scholarship from the Indian Government study Ph.D. (Economics)

at Bangalore University from [email protected]

N. Rangaswamy Ph.D.

Professor & Chairman , Department of Economics, Bangalore University, Bangalore

Economics Department

Bangalore University

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Date: October 5, 2006Paper ID. 393

Paper Title: VECM Model and Modeling International Tourism Demand in

Thailand

Authors:

Chukiat Chaiboonsri

Parsert Chaitip

N Rangaswomy

Dear Prof./Dr./Mr./Ms. Chukiat Chaiboonsri,

We are pleased to inform you that your paper has been reviewed by the review

panel and has been accepted for presentation at the APIEMS 2006 conference.You may also receive comments and feedback from the reviewer. At least one

author should register by October 15, 2006. The paper with final corrections in

Microsoft Word file format should be submitted as soon as possible but no later

than October 31, 2006. The registration form is attached. We have receivedover 450 abstracts from 26 countries and we are looking forward to an exciting

conference. Thank you for your participation and we are looking forward to

seeing you in Bangkok

.

Best Wishes,

Voratas Kachitvichyanukul

APIEMS 2006 Conference Program Chair

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A

Preface

I would like to thanks Dr. N. Rangaswamy, Professor & Chairman of the

Department of Economics at Bangalore University. He is both my professorand adviser at Bangalore University for the period from 2005 to 2010. And I

would like to thanks ICCR scholarship (India government organization) that

gave funds to me for study a Ph.D.(Economics) at Bangalore University during

the same period. This working paper is a part of the study for my Ph.D. course

at Bangalore University and some part of this paper has been presented by me

in Thai conference namely both Applied Mathematics and Statistics

Conference 2006 at Long beach Garden Hotel and Spa, Muang Pattaya,Pattaya Province, Thailand from 24-26 May 2006 and APIEMS 2006

Conference at AIT . The meeting was established by the Department of

Mathematics and Statistic Thammasat University, Statistic Researching

Organization, Data Management Organization, Bio-statistic Organization and

Statistical Association of Thailand. Furthermore my grateful thanks to my

father, my mother, my wife and my relative for their help and support in every

way. This working paper was edited by Macus Vigilante, lecturer at Payap

University, Chiang Mai . So I would like to thanks you and I hope that you can

help me on my next paper. Finally my special thanks to God because He

blesses me in every day when I walk with Him.

Chukiat Chaiboonsri

1/06/2006



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B

Contents

Page

Preface A

Contents B

Tables C

Abstract 1

1. Introduction 2

2. Research Aim and Objective 3

3. Study area in this research 3

4. The methodology and research Framework4.1 The back ground concept of International Tourism Demand Model 4

4.2 Unit-Root Tests 6

4.2.1 DF-Test, ADF Test (1979) 64.2.2 Phillips-Perron Test (PP-Test:1987,1988) 7

4.2.3 The KPSS-Test (1992) 74.2.4 The DF-GLS Test (1996) 9

4.2.5 The ERS Point Optimal Test 9

4.2.6 The Ng and Perron (NP-test:2001) 11

4.3 Cointegration and Vector Error Correction Mechanism 12

5. The results of the research5.1 The results of the Unit-Root Test 16

5.2 The results of the analysis of Modeling International 17

Tourism Demand in Thailand

5.2.1 The results of the analysis of Modeling 17

International Tourism Demand in

Thailand as in long-run from VAR Model

5.2.2 The results of the analysis of Modeling International 19Tourism Demand in Thailand as in long-run

from VECM Model

5.2.3 The results of the analysis of Modeling International 21Tourism Demand in Thailand as in Short-term dynamicsbase on VECM Model

6. The conclusions of research and policy recommendations 23

Bibliography 26



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C

Tables

Page

1. Table 1: GDP of Thailand from 1997 to 2001 2

2. Table 2: No. of international tourists and revenue from tourists 2

of Thailand

3. Table 1.1: Results of Unit Root Test base on 6 test methods for all 16

variables

4. Table 1.2: Results of Unit Root Test base on 6 test methods for all 17

variables after first or second differencing

5. Table 1.3: Results of the long-run relationship in international 18

tourism demand of Thailand based onthe Johansen and Juselius (1990) methodology

(coefficients() from VAR Model)6. Table 1.4 : Results of the long-run relationship in international 20

tourism demand of Thailand base on

Johansen & Juselius (1990) methodology

(coefficients() from VECM model)7. Table 1.5 : Vector error correction estimates of modeling international 22

tourism demand in Thailand (short-term dynamics)

8. Table 1.6 : Results of the short-term dynamics in international 23

tourism demand of Thailand base on VECM model(residual based diagnostic tests on VECM model)



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VECM Model and Modeling International Tourism

Demand in Thailand*

Chukiat Chaiboonsri,

Candidate in the Indian Government Ph.D. (Economics) programat Bangalore University, 2005-2010.

[email protected]

Parsert Chaitip Ph.D.Assoc, Prof. DR. in Faculty of Economics, Chiang Mai University, Chiang Mai .

N. Rangaswamy Ph.D.Professor & Chairman , Department of Economics, Bangalore University, Bangalore

Abstract

This paper seeks short-term dynamics and long-run relationships between

international expenditure of tourists to Thailand with Economic variables such

as the number of international tourist arrivals, GDP, price of goods andservices, transportation costs, and exchange rate risk for the period 1997(Q1)-

2005(Q2). The cointegration techniques based on Johansen and Juselius (1990)

and VECM Model were used to find the long-run and short-term dynamic

relationships of the international tourist demand model in Thailand. The paperused six full standard test methods for unit roots namely ADF-Test (1979), PP-

Test (1987,1988), KPSS-Test (1992), DF-GLS Test (1996), ERS Point Optimal

Test and Ng and Perron (2001). The VECM Model has not previously beenused to estimate tourism demand models in short-term dynamics. The long-run

results indicate that growth in income (GDP) and the number of international

tourist arrivals from Thailands major tourist source markets has both positive

and negative impacts on international expenditure on tourists in Thailand whilemostly both transportation cost and real exchange rate or exchange risk have a

negative impact on international expenditure of tourists arriving in Thailand.

The short-term dynamic results indicate that it was in some ways similar and

dissimilar to the results for the long-run. Mostly the findings are consistent with

economics theory and the implication of the model may be used for policy

making.

Keyword: Thailand; tourism demand; cointegration; VECM Model.

-------------------------------------

* This paper has been presented by me namely APIEMS 2006 Conference at AIT Bangkok , Thailand

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1. Introduction

International tourism business entered an interesting period for many

countries in Asia between 1997-1998 (many Asian countries including

Thailand experienced an economic crisis during this time: Lim(2003)) . Since

1997, the Asian region has faced an economic crisis which was due to

Thailand's policy of liberalization of its international currency called BIBF in

1993 (Kriangsak, 1998). This policy resulted in a huge inflow of loans to

Thailand as follows: (1) 193.2 billion baht in 1992, (2) 253.4 billion baht in

1993, and (3) 202.4 billion baht in 1994. Most of these loans flowed into the

minor business sector such as the stock market and real estate leading to un-

productivity and unemployment. Thus, the balance of trade in Thailand was

continuous deficits because Thai products could not compete in the word

market (Bank of Thailand, 1994). In addition to this, Thailand applied an

international policy of fixing the value of its exchange rate which was

incongruent with the real value of the US dollar (Chukiat, 2003) which made

imports much greater than exports. This again contributed to continuousdeficits and an imbalance of trade. The imbalance of trade, unfeasible

international currency policy, inflow of capital to un-productive business

sectors, all contributed to the economic crisis in 1997. As a consequence,

Thailand took out a huge loan of 17.2 billion US dollars from the InternationalMonetary Fund (IMF) in the form of a Stand-by Arrangement to help its

economy. The crisis that stared in Thailand also affected its neighbors such as

the Philippines, Indonesia, Singapore and South Korea (Lim and McAleer,2001). Another effect of the economic crisis in 1997 was a decrease in

Thailand's GDP from 1997 to 2001 as can be seen in the table below ( table 1.1).

Table 1. : GDP of Thailand from 1997 to 2001

Year GDP

(million baht)

1997 3,073,615

1998 2,749,684

1999 2,871,980

2000 3,008,662

2001 3,072,925(Source: National Economic and social Development Board of Thailand)

Table 2. : No. of international tourists and Revenue from tourists of Thailand

Year No. of Tourists

(millions)

Revenue from Tourists

(million baht)

1997 7.22 220

1998 7.76 242

1999 8.58 253

2000 9.51 285

2001 10.06 299(Source: Thailands Tourism Organization)



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Despite the economic crisis, however, the effect on the tourism industry of

Thailand was positive because of the increasing number of foreign tourists

coming to Thailand who brought in a lot of revenue to country(see table 1.2).

It is therefore concluded from this study that the international tourism industry

in Thailand was not affected by the economic crisis. For a long time now,

economists have tried to understand the international tourist consumer behavior

when travelling to various countries around the world through the demand

model. For example Barry and OHagan (1972): the demand of British tourists

travelling to Ireland; Jud, G.D. and Joseph, H. (1974): the demand of

international tourists going to Latin American; Uysal and Crompton (1984): the

demand of international tourists going to Turkey; Summary (1987): the demand

of intentional tourists going to Kenya; Kulendran, N. (1996): the demand of

international tourist going to Australia; Lim C. and M. McAleer (2000): the

demand of international tourists going to Australia; Durbarry (2002): the

demand of international tourists (French tourists) going to the UK, Spain andItaly. As well as Paresh Kumar and Narayan (2004) and Resina Katafono and

Aruna Gounder (2004): the demand of international tourists going to Fiji.

Based on much literature and articles, the aim of this paper is to find out the

international tourist consumer behavior in coming to Thailand by demandmodel during the period of 1997-2005. When understood, the results of this

consumer behavior will be useful in developing the international tourism

industry in Thailand.

2. Research Aim and ObjectiveThis research project had the aims and objectives of finding out how may

factors affect the international tourist's expenditure when coming to Thailand inboth the long-run and short-run. In particular, this research used the VECM

Model for estimating the short-term dynamics because this method had not

previously been used to estimate tourism demand. And to uses the international

tourism demand function to explain the consumer behavior of internationaltourists expenditure on arrival in Thailand.

3. Study Area in this researchThe scope of this research was the period 1997(Q1) -2005(Q2) and most of

the data was secondary. For the analysis of international tourist demand for

Thailand, the following seven countries were chosen: Malaysia, China,

England, Germany, France, America and Canada. Most of these countries were

sources of significant tourism income to Thailand during this period (Source:Thailands Tourism Organization). The variables used in this research were

economic variables such as the expenditure of international tourists in Thailand,

the number of international tourist arrivals in Thailand, the GDP of the

countries of origin of international tourists arriving in Thailand, the world price

of kerosene-type jet fuel, the relative price of Thailand with the country oforigin of international tourists caming to Thailand and both the real exchange



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rate and the exchange rate risk of Thailand with the country of origin of

tourists.

4. The methodology and research Framework

4.1 The background concept of International Tourism Demand

ModelThe concept of theory has been used in international tourist demand

since 1950 but the estimation of international tourist demand by the

econometric method was first used by Artus (1972). After that a lot of research

on international tourist demand functions used the econometric method.

Researchers who used this method are Archer (1976), Crouch (1994), Walsh

(1996), Lim (1997), Inclair (1998), Lise & Tol (2002), McAleer (2001,2003),

and Resina & Aruna (2004). Growth in international tourism is closely aligned

to economic variables which at a microeconomic level influence the

consumers decision to undertake overseas travel. Empirical research on

international tourism demand has overwhelmingly been based on aggregatetime series data which permits estimation of income and price elasticity on

inbound tourism (See Lim (1997) and McAleer (2003)). A simple origin-

destination demand model for international tourism can be written as: (equation

number (1A))

Dt =f (Yt TCt Pt) -------------------------- (1A)

Defined

Dt = is a measure of travel demand at time t ;

Yt = is a measure of income of the tourist-generating or origincountry at time t ;

TCt = is a measure of transportation costs from the origin to

destination country at time t ;

Pt = is a measure of tourism price of goods and services at time t ;And assume that (+ Yt), (-TCt), (- Pt) and explain that when income at

time t is increasing then the demand for international tourism is increasing

together. When a measure of transportation costs from the origin to destinationcountry at time t is increasing then the demand for international tourism is

decreasing. And when a measure of tourism price of goods and services is

increasing then the demand for international tourism is decreasing. And the

equation (1A) can be expressed in log-linear (or logarithmic) [equation number(2A)] .

ln Dt = + ln Yt + ln {F1t or F2t } + ln {RPt, ERt or RERt }

+ ln Dt -1 + lnCPt + u t ----------- (2A)



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where

ln Dt = logarithm of short-term quarterly tourist arrivals (or

demand) from the origin to destination country at time t ;

ln Yt = logarithm of real GDP in country of origin at time t ;

lnF1t = logarithm of real round-trip coach economy airfares in

Neutral Units of construction (NUC) between original

country and destination country at time t ;

lnF2t = logarithm of real round-trip coach economy airfares in

country of origin currency between country of origin and

destination country at time t ;

ln RPt = logarithm of relative prices (or CPI of destination country

/CPI of country of origin) at time t ;

lnERt = logarithm of exchange rate (country of origin per

destination country) at time t ;lnRERt = logarithm of real exchange rate [or CPI (destination

country)/CPI (country of origin)*1/ER] at time t ;

lnCPt = logarithm of competitive prices [using CPI (destination

country) /(other destination country)]

u t = independently distributed random error term, with zero

mean and constant variance at time t ;

And defined that

, , , ,, = parameters to be estimated; > 0, < 0, < 0,0 0 (Substitutes) and < 0(complements)

And this research or the VECM model and Modeling International

Tourism Demand in Thailand modified from equation (2A) as well as can be

written as equation (3A) .

ln(Expdt) = + ln D1t + ln (GDPt) + ln (POt) + ln (RPt)+ ln(RERt) + (SDRt)+ u t ----------- (3A)

where

lnExptt = logarithm of expenditure of tourist arrivals from the country

of origin (each of the seven countries) to destination

country

(Thailand) at time t ;

ln D1t = logarithm of quarterly tourist arrivals from the country of

origin (each of the seven countries) to destination country

(Thailand) at time t ;

ln GDPt = logarithm of real GDP of the country of origin (each of theseven countries) at time t ;



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lnPOt = logarithm of price of jet fuel at time t ;

ln RPt = logarithm of relative prices (or CPI of thedestination

country: (Thailand) /CPI of the country of origin:

(each of the seven countries) at time t ;

lnRERt = logarithm of real exchange rate [ or CPI(Thailand)

/CPI (each of the seven countries)*1/ER] at time t ;

SDRt = standard deviations of exchange rates

(country of origin (each of the seven countries) per

destination country (Thailand)) at time t ;

u t = independently distributed random error term, with

zero mean and constant variance at time t ;

And defined that

, , , ,,, = parameters to be estimated;SDRt = exchange rate risk;

> 0, < 0, < 0, < 0, < 0, > 0;

4.2 Unit-Root Tests

This research tested the stationarity of all variables used in the

International Tourism Demand Model by standard tests for unit roots. Such as

the ADF-Test (1979), PP-Test (1987,1988), KPSS-Test (1992), DF-GLS Test

(1996), the ERS Point Optimal Test and Ng and Perron (2001).

4.2.1 DF-Test, ADF Test (1979)The DF-Test uses three equation for unit root test in Yt and Yt is time

series data.

DYt = Yt-1 + Ut --------- (1B) [ No Intercept Term ]DYt = t+ Yt-1 + Ut ------- (2B) [ Intercept Term ]DYt = 1+ t+ Yt-1 + Ut -------- (3B) [ Intercept + Trend ]

Where

= ( - 1) : null hypothesis is that = ( - 1) = 0 (Non-stationary data (=1))

if > Mackinnon statistics conclude that time series data is stationaryor I(d) = I(0) otherwise rejected null hypothesis is that = ( - 1) = 0 or [ = 1] because if has a statistics significance at any level then 0 ( 1).

if < Mackinnon statistics conclusion that time series data is non-stationary or I(d) = I(d) as well as accepted null hypothesis is that = ( - 1) =0 or [ = 1 ] because if has not a statistics significance at any level then = 0 ( = 1).



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The ADF-Test was used for unit root test when found that higher order

autocorrelation in time series data. Before using the ADF-Test, dw should be

checked with statistics from DF-Test equation (2B) and (3B).

D Yt = 1 + t + Yt-1 + imi=1Yt-i + t ------- (4B)

When added term (i mi=1Yt-i) in equation (4B) then t-statistics value of before Yt-1 to be change as well as all t-statistics value of them to be changetoo. So ADF-Test corrects for higher order serial correlation by adding laggeddifferenced terms on the right-hand side. The hypothesis test for unit root in

time series data by ADF-Test method as for the DF-test method and same

conclusion about time series data are stationary or non-stationary.

4.2.2 Phillips-Perron Test (PP-Test:1987,1988)

This test method for unit root was developed by Phillips and Perron(1988) and they proposed a nonparametric method for controlling for higher-

order serial correlation in time series data.

D Yt = + t Yt-1 + t ------- (5B)

The PP-test makes a correction to the t-statistic of the coefficient fromthe AR(1) regression to account for the serial correlation in equation (5B). Thecorrection is nonparametric since it uses an estimate of the spectrum of

equation (5B) at frequency zero that is robust to heteroskedasticity and

autocorrelation of unknown form.

j = (1/ T) Tt= j + 1 * t * t- j --------- (6B)

W2= 0 + 2 q j= 1 [ 1- j/(q+1) ] j ------ (7B)

where

W2

= Newey-west heteroskedasticity autocorrelation

consistent estimation

j = coefficient from AR(1) in equation (5B)* t * t- j = error term received from equation (5B)q = floor(4 (T/100)

2/9 ), [ q is the truncation lag ]

And the PP-Test (tpp) has a t-statistic is computed as equation (8B) as well

as where tb , sb are the t-statistics and standard error of ( t) received fromregress in equation (5B) and s* is the standard error received from regress in

same equation.

where



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PP-Test (tpp) =[(01/2 tb) / (W )][(W 2 - 0) T sb/ (2 W s*)]--(8B)

The asymptotic distribution of the PP-Test (tpp) is the same as the ADF-

Test. And the hypotheses to be tested are follow up:

H0 : null hypothesis as time series data is non-stationary

H1 : time series data is stationary

if PP-Test (tpp) > Mackinnon statistics conclusion that time series

data is stationary otherwise rejected the null hypothesis is that non-stationary

data.

if PP-Test (tpp) < Mackinnon statistics then conclude that time

series data is non-stationary as well as accepted null hypothesis.

4.2.3 The KPSS-Test (1992)The KPSS-Test for unit root test was produced by Kwiatkowski, Phillips,

Schmidt and Shin (1992). And the KPSS statistic is based on the residuals from

the OLS regression of Yt on the exogenous variables X t (See equation (9B))

and X t is a random walk :Xt = Xt-1 + t .

Yt = Xt + t ------- (9B)where

X t : Xt = a 0 + b0 t + t [ intercept and trend ]

X t : Xt = a 0 + t [ intercept ] t : is a stationary random errorYt : is data test stationary or non-stationary

Regress Yt on X t or regress Yt on a constant and a trend then obtain the

residual t from equation (9B) as well as take this residual to calculate in theKPSS statistic (See equation (10B)).

KPSS = T-2S S

2t/ (s

2(L)) ------- (10B)

where

T = is the sample size

St = ti = 1 i , t = 1,2,., Ts

2(L) = T

-1Tt = 1 2t + 2 T-1Ls = L w(S,L) Tt = s+1 t t- sw(S,L) = is an optional weighting function corresponding to the

choice of a spectral window.

w(S,L) = 1- s / (L +1) in estimation (See Newey and west, 1987 :

and Kwiatkowski al.,1992, for more details).

L = the number of truncation (lags) is chosen



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The KPSS-test method test for unit root has the hypothesis to be tested are

H0 (null hypothesis) and H1 .

H0 : time series is stationary

H1 : time series is non-stationary

if KPSS-statistics > Quantities of distribution of KPSS statistics

table as rejected H0 or rejected null hypothesis and accepted H1 then conclusion

that Yt is non-stationary.

if KPSS-statistics < Quantities of distribution of KPSS statistics

table and accepted H0 or rejected H1 then conclusion that Yt is stationary.

4.2.4 The DF-GLS Test (1996)This test suggested by Elliott, Rothenberg, Stock (1996) and the DF-GLS

Test is performed by testing the hypothesis a 0 = 0 ( =1, a 0 = ( - 1) inequation (11B) to be start for this test (because PP-Test and ADF-Test have low

power for unit root test and conclude that tests for unit root need to bedeveloped (DeJong et al(1992)) as well as Madala and Kim (1998) suggested

that DF-GLS Test is a one method that higher power for unit root test).

D Ydt = a 0Y

dt + a 1D Y

dt-1 +.+ a pD Y

dt-p + t ------- (11B)

Where Yd t is the locally de-trend series Y t and Y

d t = Y t - B

*0 - B

*1t as

well as where (B*

0 , B*

1t) are obtained by regressing y* on z* . And where y*= [ y1, (1- *L)y2,., (1- *L)yT ] as well as z* = [ z1, (1- *L)z2,., (1-*L)zT ] .

Where

L = the lag operator

* = 1+c*/ T , c* = -7 : in the model with drift, c* = 13.5 : inthe linear trend case

z* = (1,t)

Both DF-GLS and ADF-Test have non-stationary as null hypothesis andto show below that :

H0 : a 0= 0 : [time series data is non-stationary ]

H1 : a 0 0 : [time series data is stationary ]

if a 0> Critical values for the DF-GLS Test for a model with linear

trend (Elliott et al.1996) and rejected H0 or rejected null hypothesis as well as

conclusion that time series data is stationary or I(d) = I(0) .if a 0

< Critical values for the DF-GLS Test for a model with linear

trend (Elliott et al.1996) and accepted H0 or accepted null hypothesis as well asconclusion that time series data is non-stationary or I(d) = I(d) .



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4.2.5 The ERS Point Optimal Test

The ERS Point Optimal Test is based on quasi-differencing regression in

equation (12B). When a time series has an unknown mean or linear trend and

this method to start from equation (12B).

d(yt | a) = d(xt | a)/ (a) + t ------- (12B)

where

- d(yt | a) and d(xt | a)are quasi-differenced data for yt and xt

- t : error term that is independently and identicallydistributed

- yt : is time series data are tested- xt : contain a constant only or both a constant and time

trend

- (a) : the coefficient to be estimated in equation (12B)- a : a* = 1-7/T when xt contains a constant

- a : a* = 1-13.5/T when xt contains a constant andtime trend

The P(T) statistics was used in ERS Point Optimal Test for unit root test in time

series data and show it below that : (See equation 12B)

P(T) statistics = ((SSR(a*)) (a*)SSR(1)) /f0 ------ (13B)

where

SSR = the sum of squared residuals from equation (12B)

f0 = is a frequency zero spectrum estimation

or

f0

= T-1j =-(T- 1)* (j) . k(j/t),

j = the j-th sample autocorvariance of the tt = a truncation lag in the covariance weighting

* (j) = Tt = j+1( t t- j)/ T, T = the number of observationk = a kernel function (for detail see Eview 5.1 (2004))

and where

Bartlett : k(x) = [ 1-|x| if |x| < or = 1, 0 = otherwise ]

Parzen : k(x) = [1-6x2

+6|x|3if 0 < or = |x| < or = (1/2)

2(1- |x|

3)

if (1/2) Critical values for ERS test statistic arecomputed by interpolating the simulation result provided by ERS (1996,table

1,p.825) for T = {50, 100, 200, } then accepted H0 : = 1 : [ timeseries data is non-stationary ] and said that time series data is non-stationary.

if P(T) statistics < Critical values for ERS test statistic are computed

by interpolating the simulation result provided by ERS (1996,table 1,p.825) for

T = {50, 100, 200, } then accepted H1 : = a*: [ time series data isstationary ] and said that time series data is stationary (perception : the ERS

Test was used to test unit root for time series data have big simple size at least

more than 50 observations).

4.2.6 The Ng and Perron (NP-test:2001)Ng and Perron(2001) developed from four test statistics based on the

GLS detrended data Yd t and these test statistics are modified forms of Philips

and Perron Za and Zt statistics, Bhargava(1986) R1 statistic and the ERS PointOptimal statistic. This method to start by first define term follow that : (See

equation 14B).

K = Tt = 2 (Ydt-1)2 / T2 ----------- (14B)

And modified statistics of Ng and Perron(2001) be written as, (four

statistics were used to test for unit root in time series data : MZd

a, MZd

t, MSBd

and MPd

t).

where

MZda = (T

-1(Y

dt)

2- f0) / (2k)

MZdt = MZ

da . MSB

d

MSBd

= (k /f0)1/2

and



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MPd

t = { (c*2

k c*T-1

(Ydt)

2) /f0 if xt = 1 or z* = 1,

(c*2

k + (1-c*)T-1

(Ydt)

2) /f0 if xt = (1,t) or z* = (1,t) }

where

c* = { -7 if xt = 1 or z* = 1, -13.5 if xt = (1,t) or

z* = (1,t) }

f0 = T-1j =-(T- 1)* (j) . k(j/t), j = the j-th sample autocorvariance of the tt = a truncation lag in the covariance weighting

* (j) = Tt = j+1( t t- j)/ T, T = the number of observationor

f0 = kernel- based sum-of-covariance estimator, and

autoregressive spectral density estimators

The null hypothesis of Ng and Perron(2001) Test for unit root test in time

series data can show below that :

H0 : is time series data is non-stationary

H1 : is time series data is stationary

if MZd

a, MZd

t, MSBd

,MPd

t statistics > Critical values

of Ng and Perron((2001),table 1) then accepted H0 : [ time series data is

non-stationary ] and said that time series data is non-stationary.

if MZda, MZdt, MSBd,MPdt statistics < Critical valuesof Ng and Perron((2001),table 1) then rejected H0 : [ time series data is

non-stationary ] one other hand accepted H1 : [ time series data is stationary ]

and said that time series data is stationary.

4.3 Cointegration and Vector Error Correction Mechanism (VECM

model)

Engle and Granger (1987) pointed out that a linear combination of two

variables or more variables are nonstaionary series may be stationary. If such a

stationary, or I(0), linear combination exists, the stationary linear combination

is called the cointegratting equation and may be interpreted as long-runequilibrium relationship between equation. The problems with Engle-Granger

two step procedure in co-integration approach. For example if assumed that

Economic theory can guide in determining the dependent and the independent

variable, like in the consumption function (equation number (1C)).

Ct = 0 + 1Yt + ut ------------- (1C)

But if equation (1C) has three variables (Y, W, Z) then these are threepossible long run relationships then can show equation numbers (2C), (3C) and

(4C).



18/34

Yt = 0 + 1Wt + 2Zt + ut -------------- (2C)Zt = 0 + 1Yt + 2Wt + ut -------------- (3C)Wt = 0 + 1Wt + 2Zt + ut -------------- (4C)

So that the co-integration approach of EG can not do it in more than two

variables and these weaknesses limit applicability of the this approach. To

introduce a technique that consider co-integration not only between pairs of

variables, but also in a system this technique is the ML approach of Johansen

and Juselius (1990). The Johansen and Juselius approach start at model Ztunrestricted vector auto-regression (VAR) involving up to K-lags of Zt:(equation number (5C))

Zt = A1 Zt-1 + + Ak Zt-k + ut , ut ~IN(0,) ------------- (5C)

Where Zt is (n X 1) and each of the Ai is an (n x n) matrix of parameters.

Equation (5C) has been expressed in first differenced form and it is convenient

to rewrite the equation (5C) to be (6C) as well as described below.

Zt = 1Zt-1 + + k-1Zt-k+1 + Zt-k+ ut -- (6C)

where

i = -(I - A1 - . - Ai), (i = 1,,k-1) = -(I - A1 - . - Ai)

= and is adjustment coefficients of disequilibriumand is Co-integrating vectors(and to be found).

This way of specifying the system contains information on both the short-

and long-run adjustment to changes in Zt (Zt) and rewriting (6C) as :

(equation number (7C)).

Zt + Zt-k= 1Zt-1 + + k-1Zt-k+1 + ut ----- (7C)

It is possible to correct for short-run dynamics by regressing

Zt and Zt-kseparately on the right-hand side of (7C) . That is, the vectors R 0 t and R k t are

obtained from :(equation number (8C) and number (9C))

Zt = P1Zt-1 + + Pk-1Zt-k+1 + R0t --- (8C)

Zt-k= T1Zt-1 + + Tk-1Zt-k+1 + Rkt ---- (9C)

Which can then be used to form residual (product moment) matrices

:(equation number (10C))

Sij = T

1

T

i=1R it R/

jt ,(i,j = 0,k) -------------------- (10C)



19/34

The maximum likelihood estimate of is obtained as the eigenvectorscorresponding to the r largest eigenvalues from solving the number (11C).

Sjj - Sji Sii-1 Sij= 0 -------------------- (11C)

Which gives the n eigenvalues ^1> ^2 >.> ^n and thecorresponding eigenvectors ^ = (^1 >,.,> ^n). Those r elements in ^which determine the linear combinations of stationary relationships can be

denoted ^=(^1 >,.,>^r), that is, these are the cointegration vectors. TheVECM model has been develop by Hendry (1995) and he used the Johansen-

Juselius (JJ) methodology to study long-run relationship among M1, the price

level, output and interest rate in Canada. The VECM modelling procedure form

can be written and it begins by defining an unrestricted vector autoregression

(VAR) involving up to k-lags as well as can show below that : (See equation

(12C))

Zt = A1Zt-1 ++ A kZt-k + 1 ------- (12C)

And can be written in form of vector autoregressive in difference and error

correction components as follow equation (13C) as well as this equation was

called that VECM models.

Zt = C +1Zt-1 ++ k-tZt-k+1 + Zt-k+ 1 ----- (13C)

where

Zt = variables were used in the VECM models

Zt = difference term of variables were used in the VECM models

jZt-j = the vector autoregressive(VAR) component in firstdifference

Zt-p = error-correction componentsC = vector of constant is an (n x 1)

1 = vector of white noise error termsj = an (n x n) matrix for short term adjustment coefficients

among variables with k-1 number of lags

= (the value of to be found), = an (n x n) matrix

where

= an (n x r) matrix which represents the speed ofadjustment coefficient of the error correction

mechanism.

= an (n x r) matrix of cointegrating vectorsrepresents up to r cointegrating relationship in the



20/34

multivariate model which represent long-run

steady solutions.

The simply of VECM model can rewrite based on concept of this research

follow by equation (14C) and this equation described below.

Xt = +(L)Xt + DZt + Xt-1 + 1 ----- (14C)Xt = +(L)Xt + DZt + Xt-1 + 1 ----- (15C)

where

Xt = [ all variables were used in model ]

Xt = [ differencing in all variables were used in model]

Zt = [constant term, dummies variable and other variables ]

(L) = Matrix of parameters for n order lags process1 = error term of equation = [ to be found ] and where

= speed of adjustment to equilibrium = cointegrating vector in long-run

And define that

Xt= [ln(Expdt), ln(D1t), ln(GDPt), ln(POt),

ln(RPt), ln(RERt), (SDRt)]Xt =[ln(Expdt), ln(D1t), ln(GDPt), ln(POt), ln(RPt),

ln(RERt), (SDRt)]

The simply of VECM model can rewrite based on concept of this research

follow by equation (16C) again and this equation described below.

ln(Expdt) ln(Expdt)

ln(D1t) ln(D1t)ln(GDPt) ln(GDPt)

ln(POt) = + (L) ln(POt) DZt + Xt-1 + 1 ----- (16C)ln(RPt) ln(RPt)

ln(RERt) ln(RERt)

(SDRt) (SDRt)

where



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DZt = [constant term ]

(L) = Matrix of parameters for n order lags process1 = error term of equation = [ to be found and where

= speed of adjustment to equilibrium = cointegrating vector in long-run

And the simply of VECM model in shortterm dynamics was used in this

research can be rewrite as equation (17C) as well as descried below.

ln(Expdt) ln(Expdt)

ln(D1t) ln(D1t)

ln(GDPt) ln(GDPt)

ln(POt) = + (L) ln(POt) + DZt + + 1 ----- (17C)ln(RPt) ln(RPt)ln(RERt) ln(RERt)

(SDRt) (SDRt)

where

DZt = [constant term ]

(L) = Matrix of parameters for n order lags process

1 = error term of equation = speed of adjustment to equilibrium = coefficient of speed of adjustment to equilibrium

5. The results of research

5.1 The results of Unit-Root TestThis paper determined the order of integration of the variables by 6

standard methods test for unit root - ADF-Test (1979), PP-Test (1987,1988),

KPSS-Test (1992), DF-GLS Test (1996), ERS Point Optimal Test and Ng &Perron (2001). And if variable are integrated of the same order than apply the

Johansen-Juselius (1990) maximum likelihood method of obtain the number of

cointegrating vector(S) for long-run and use VECM model for short-term

dynamics. The results of the unit root tests based on 6 standard methods areshown in table 1.1. All variables were used in international tourism demand

model of Thailand were integrated of order (d) except that both the GDP of

Malaysia and the RER of China were integrated to the order (0).



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Table 1.1: Results of Unit Root Test base on 6 test methods for all

variables

variables Malaysia China England German France America Canad

Expd I(d) I(d) I(d) I(d) I(d) I(d) I(d)

D1 I(d) I(d) I(d) I(d) I(d) I(d) I(d)GDP I(0) I(d) I(d) I(d) I(d) I(d) I(d)

Po I(d) I(d) I(d) I(d) I(d) I(d) I(d)

RP I(d) I(d) I(d) I(d) I(d) I(d) I(d)

RER I(d) I(0) I(d) I(d) I(d) I(d) I(d)

SDR I(d) I(d) I(d) I(d) I(d) I(d) I(d)

From: computation

And when first differencing or second differencing in all variables (except

both GDP of Malaysia and RER of China) were used in this model as well as

the order of integrated in all variables changed. The results of unit root test

based on 6 methods after first differencing or second differencing are shown intable1.2.

Table 1.2: Results of Unit Root Test base on 6 test methods for allvariables after first or second differencing

From : computed

After first differencing or second differencing in all variables were used in

international tourism demand model of Thailand were integrated of order (1)except the RP of China, the RP German, the RP of France, the RP of America

and the RP of Canada were integrated of order (2).

5.2 The results of the analysis of Modeling International Tourism Demand

in Thailand

5.2.1 The results of the analysis of Modeling International Tourism

Demand in Thailand as in long-runfrom VAR ModelEstimates of long-run cointegrating vectors of modeling

international tourism demand in Thailand are given in table 1.3 and this method

was based on Johansen and Juselius (1990) as well as the method derived from

VAR model concept. In Malaysia as the long-run cointegrating vectors

suggested, ln(RPt) has a positive impact on international tourist expenditure inThailand or impact on tourism demand in Thailand except ln(D t-1), ln(POt),

variables Malaysia China England German France America Canada

Expd I(1) I(1) I(1) I(1) I(1) I(1) I(1)

D1 I(1) I(1) I(1) I(1) I(1) I(1) I(1)

GDP I(0) I(1) I(1) I(1) I(1) I(1) I(1)

Po I(1) I(1) I(1) I(1) I(1) I(1) I(1)RP I(1) I(2) I(1) I(2) I(2) I(2) I(2)

RER I(1) I(0) I(1) I(1) I(1) I(1) I(1)

SDR I(1) I(1) I(1) I(1) I(1) I(1) I(1)



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ln(RERt) and ln(SDRt) have a negative impact on this model. The results imply

that in long-run when ln(RPt) increases 1% then Malaysias tourists spending

on goods and services in Thailand increases by 19.38%. Otherwise when ln(D t-1), ln(POt), ln(RERt) and ln(SDRt) increase by 1% then Malaysian tourist

spending on goods and services in Thailand decreases by 1.71%, 0.68%, 3.75%

and 0.43%. In China as long-run cointegrating vectors suggested, ln(Dt-1),

ln(GDPt) and ln(SDRt) have a positive impact on international tourists

expenditure in Thailand or impact on tourism demand in Thailand. Otherwise

both ln(POt) and ln(RERt) have negative impacts on this model. The results

imply that in the long-run when ln(Dt-1), ln(GDPt) and ln(SDRt) increase by 1%

then Chinese tourists spending on goods and services in Thailand increases by

5.82%, 8.61% and 10.75%. Otherwise when both ln(PO t) and ln(RERt) increase

by 1% then Chinese tourist spending on goods and services in Thailand

decreases by 2.63% and 16.72%.

Table 1.3: Results of the long-run relationship in international tourism demandof Thailand based on the Johansen and Juselius (1990)

methodology

(coefficients() from VAR Model).

Country Ln(Dt-1) Ln(GDPt) Ln(POt) Ln(RPt) Ln(RERt) Ln(SDRt)

Malaysia -1.71 - -0.68 19.38 -3.75 -0.43

China 5.82 8.61 -2.63 - -16.72 10.75

England -6.14 6.02 -0.15 -3.83 -3.19 -0.13

German -3.96 -0.02 0.08 - -0.12 -0.32

France -5.34 6.86 -1.17 - 0.17 -0.55

America 3.14 -0.81 -0.42 - 2.14 0.33

Canada -59.35 104.44 -4.60 - 22.17 -1.94

From : computation

In England as the long-run cointegrating vectors suggested, ln(Dt-1),

ln(POt), ln(RPt), ln(RERt) and ln(SDRt) have a negative impact on international

tourist expenditure in Thailand or impact on tourism demand in Thailand.

Otherwise ln(GDPt) has a positive impact on this model. The results imply thatin the long-run when ln(Dt-1), ln(POt), ln(RPt), ln(RERt) and ln(SDRt) increase

by 1% then English tourists spending on goods and services in Thailand

decreases by 6.14%, 0.15%, 3.83%, 3.19% and 0.13%. Otherwise when

ln(GDPt) increases by 1% then English tourist spending on goods and services

in Thailand increases by 6.02%. In Germany as long-run cointegrating vectors

suggested, ln(Dt-1), ln(GDPt), ln(RERt) and ln(SDRt) have negative impacts oninternational tourist expenditure in Thailand or impact on international tourism

demand model. Otherwise ln(POt) has a positive impact on this model. The

results imply that in long-run when ln(Dt-1), ln(GDPt), ln(RERt) and ln(SDRt)

increase by 1% then German tourist spending on goods and services inThailand decreasing 3.96%, 0.02%, 0.12% and 0.32%. Otherwise when



24/34

ln(POt) increases by 1% then German tourist spending on goods and services in

Thailand increases by 0.08%. In France, as the long-run cointegrating vectors

suggested, ln(Dt-1), ln(POt), and ln(SDRt) have negative impacts on

international tourist expenditure in Thailand or impact on the international

tourism demand model. Otherwise both ln(GDPt) and ln(RERt) have positive

impacts on this model. The results imply that in the long-run when ln(D t-1),

ln(POt), and ln(SDRt) increase by 1% then French tourist spending on goods

and services in Thailand decreases by 5.34%, 1.17% and 0.55%. Otherwise

when both ln(GDPt) and ln(RERt) increase by 1% French tourist spending on

goods and services in Thailand increases by 6.86% and 0.17%. In America as

the long-run cointegrating vectors suggested, ln(Dt-1), ln(RERt), ln(SDRt) have

positive impacts on international tourist expenditure in Thailand or impact on

the international tourism demand model. Otherwise both ln(GDPt) and ln(POt)

have negative impacts on this model. The results imply that in the long-run

when ln(Dt-1), ln(RERt), ln(SDRt) increase by 1% then American tourist

spending on goods and services in Thailand increases by 3.14%, 2.14% and

0.33%. Otherwise when both ln(GDPt) and ln(POt) increases by 1% thenAmerican tourist spending on goods and services in Thailand decreases 0.81%

and 0.42%. And finally for Canada, as the long-run cointegrating vectors

suggested, ln(Dt-1), ln(POt), and ln(SDRt) have a negative impact on

international tourist expenditure in Thailand or impact on the internationaltourist demand model. Otherwise both ln(GDPt) and ln(RERt) have positive

impacts on this model. The results imply that in long-run when ln(D t-1), ln(POt),

and ln(SDRt) increase by 1% then Canadian tourist spending on goods andservices in Thailand decreases by 59.35%, 4.60% and 1.94%. Otherwise when

both ln(GDPt) and ln(RERt) increase by 1% then Canadian tourist spending on

goods and services in Thailand increases by 104.44% and 22.17%.

5.2.2 The results of the analysis of Modeling International Tourism

Demand

in Thailand as in long-runfrom VECM ModelEstimates of long-run cointegrating vectors of modeling international

tourism demand in Thailand are given in table 1.4 and this method was based

on Johansen and Juselius (1990) as well as the method derived from the VECM

model concept. In Malaysia as the long-run cointegrating vectors suggested,

ln(RPt-1) has a positive impact on international tourism expenditure in Thailandor impact on tourism demand in Thailand, but ln(Dt-1), ln(POt-1), ln(RERt-1) and

ln(SDRt-1) have a negative impact on this model. The results imply that in the

long-run when ln(RPt-1) increase by 1% then Malaysian tourist spending on

goods and services in Thailand increases by 31.19%. Otherwise when ln(Dt-1),ln(POt-1), ln(RERt-1) and ln(SDRt-1) increase by 1% then Malaysian tourist

spending on goods and services in Thailand decreases by 2.04%, 0.97%, 5.72%

and 1.40%. In China, as the long-run cointegrating vectors suggested, ln(GDPt-

1) has a positive impact on international tourist expenditure in Thailand or

impact on tourist demand in Thailand. Otherwise ln(D t-1), ln(POt-1) andln(SDRt-1) have a negative impact on this model. The results imply that in the



25/34

long-run when ln(GDPt-1) increase by 1% then Chinese tourist spending on

goods and services in Thailand increases by 0.97%. Otherwise when ln(Dt-1),

ln(POt-1) and ln(SDRt-1) increase by 1% then Chinese tourist spending on goods

and services in Thailand decreases by 1.07%, 0.26% and 0.46%. In England as

the long-run cointegrating vectors suggested, ln(Dt-1), ln(POt-1), ln(RPt-1),

ln(RERt-1) and ln(SDRt-1) have negative impacts on international tourist

expenditure in Thailand or impact on the tourisms demand in Thailand.

Otherwise ln(GDPt-1) has a positive impact on this model. The results imply

that in the long-run when ln(Dt-1), ln(POt-1), ln(RPt-1), ln(RERt-1) and ln(SDRt-1)

increase by 1% then English tourist spending on goods and services in Thailand

decreases by 5.07%, 0.53%, 1.90%, 3.53% and 0.09%. Otherwise when

ln(GDPt-1) increases by 1% then English tourist spending on goods and services

in Thailand increases by 6.45%.

Table 1.4 : Results of the long-run relationship in international tourism demandof Thailand base on Johansen & Juselius (1990) methodology

(coefficients() from VECM model).

From : computation

Variables Malaysia China England German France America Canadaln(Expdt-1) 1.00 1.00 1.00 1.00 1.00 1.00 1.00

ln(Dt-1) -2.04***(-9.39)

-1.07***(-8.32)

-5.07***(-33.01)

-2.01***(-25.67)

2.27***(5.27)

0.31(1.65)

18.35***(7.06)

ln(GDPt-1) - 0.97***

(9.14)

6.45***

(34.97)

1.96***

(3.87)

1.11

(0.94)

1.80***

(4.63)

-77.04***

(-8.25)ln(POt-1) -0.97***

(-10.95)-0.26***(-3.05)

-0.53***(-24.30)

-0.10***(-3.11)

-0.35***(-2.18)

-0.28***(-2.80)

2.43***(2.03)

ln(RPt-1) 31.19***

(6.36)

- -1.90***

(-5.07)

- - - -

ln(RERt-1) -5.72***(-8.71)

0.38(1.22)

-3.53***(-30.86)

0.24***(5.68)

0.40***(5.00)

3.14***(12.76)

-52.12***(-6.01)

ln(SDRt-1) -1.40***(-2.80)

-0.46**(-1.92)

-0.09***(-14.73)

-0.17***(-9.93)

1.00***(8.28)

-0.03(-1.57)

-2.49***(-2.93)

C -7.21 -18.12 -18.27 -10.99 -49.27 -27.14 173.25Figures in parenthesis are t-statistics (*** = Sig. at 99%, **= Sig. at 95%, * =Sig. at 90%).

In Germany, as the long-run cointegrating vectors suggested, ln(Dt-1),ln(POt-1) and ln(SDRt-1) have a negative impact on international tourist

expenditure in Thailand or impact on the international tourism demand model.

Otherwise both ln(GDPt-1) and ln(RERt-1) have positive impacts on this model.

The results imply that in long-run when ln(Dt-1), ln(POt-1) and ln(SDRt-1)

increase by 1% then German tourist spending on goods and services in

Thailand decreases by 2.01%, 0.10% and 0.17%. Otherwise when both

ln(GDPt-1) and ln(RERt-1) increasing 1% then German tourist spending on

goods and services in Thailand increases by 1.96% and 0.24%. In France as the

long-run cointegrating vectors suggested, ln(Dt-1), ln(RERt-1) and ln(SDRt-1)

have positive impacts on international tourist expenditure in Thailand or impacton the international tourism demand model. Otherwise ln(POt-1) has a negative



26/34

impact on this model. The results imply that in the long-run when ln(Dt-1),

ln(RERt-1) and ln(SDRt-1) increases by 1% then French tourist spending on

goods and services in Thailand increases by 2.27%, 0.40% and 1.00%.

Otherwise when ln(POt-1) increases by 1% then French tourist spending on

goods and services in Thailand decreases by 0.35%. In America, as the long-

run cointegrating vectors suggested, both ln(GDPt-1) and ln(RERt-1) have

positive impacts on international tourist expenditure in Thailand or impact on

the international tourism demand model. Otherwise ln(POt-1) has a negative

impact on this model. The results imply that in long-run when both ln(GDPt-1)

and ln(RERt-1) increases 1% then Americatourist spending on goods and

services in Thailand increases by 1.80% and 3.14%. Otherwise when ln(POt-1)

increase by 1% then American tourist spending on goods and services in

Thailand decreases by 0.28%. And for Canada, as the long-run cointegrating

vectors suggested, both ln(Dt-1) and ln(POt-1) have positive impacts on

international tourist expenditure in Thailand or impact on the international

tourisms demand model. Otherwise ln(GDPt-1), ln(RERt) and SDRt have

negative impacts on this model.The results imply that in long-run when bothln(Dt-1) and ln(POt-1) increase by 1% then Canadian tourist spending on goods

and services in Thailand increases 18.35% and 2.43%. Otherwise when

ln(GDPt-1), ln(RERt-1) and SDRt-1 increase by 1%, Canadian tourist spending on

goods and services in Thailand decreases by 77.04%, 52.12% and 2.49%.

5.2.3 The results of the analysis of Modeling International Tourism Demand in

Thailand as in Short-term dynamics base on VECM modelEstimates of short-term dynamics base on VECM model are given in

table 1.5 and the method is based on Johansen and Juselius (1990) as well as

the method derived from VECM model concept. The results of short-term

dynamics indicate that D(lnD1(-2)) has a negative impact on internationaltourist spending on goods and services in Thailand. The results imply that when

D(lnD1(-2)) of both America and Canada increases by 1% then American and

Canadian tourist spending on goods and services in Thailand decreases by

0.87% and 0.51%. The results of short-term dynamics indicate that D(lnPO(-1))has a negative impact on international tourist spending on goods and services in

Thailand. The results imply that when D(lnPO(-1)) increases by 1% then

Malaysian tourist spending on goods and services in Thailand decreases by1.26%. The results of short-term dynamics indicate that D(lnPO(-2)) has a

positive impact on international tourist spending on goods and services in

Thailand. The results imply that when D(lnPO(-2)) increase by 1% then

English tourist spending on goods and services in Thailand increases by 1.36%.The results of short-term dynamics indicate that D(lnRP(-1)) has a negative

impact on international tourist spending on goods and services in Thailand. The

results imply that when D(lnRP(-1)) of Thailand increases by 1% then Chinese

tourist spending on goods and services in Thailand decreases by 1.46%. The

results of short-term dynamics indicate that D(lnRP(-2)) has a positive impact

on international tourist spending on goods and services in Thailand. The results

imply that when D(lnRP(-2)) of Thailand increases by 1% then Canadian



27/34

tourist spending on goods and services in Thailand increases by 0.59%. The

results of short-term dynamics indicate that D(lnRER(-1)) has a positive impact

on international tourist spending on goods and services in Thailand.

Table 1.5 : Vector error correction estimates of modeling international

tourism demand in Thailand (short-term dynamics)from :computed

Independent

Variables. Malaysia China England Germany France America Canada

-0.503902 0.177159 0.309506** 1.203675* -0.528811** -0.608663** 0.003052[-1.50568] [ 0.49380] [ 2.05065] [ 1.69457] [-2.01291] [-3.13838] [ 0.16009]

D(lnEXPD(-1)) -0.828076 0.110635 -0.307244 -0.308411 0.450043 0.489743 0.288242

[-1.17236] [ 0.08853] [-0.59252] [-0.29577] [ 0.58410] [ 1.32038] [ 0.84574]

D(lnEXPD(-2)) -0.556369 0.904250 -0.418149 0.396109 0.430390 0.355316 -0.494428

[-0.69160] [ 0.66984] [-0.81342] [ 0.35483] [ 0.51886] [ 0.96650] [-1.46423]

D(lnD1(-1)) 0.197669 -0.528634 0.964221 0.853267 0.321948 -0.214974 -0.349500

[ 0.28860] [-0.42086] [ 1.25351] [ 0.75856] [ 0.42000] [-0.55447] [-0.88466]

D(lnD1(-2)) 0.032269 -1.227923 0.070685 -0.700421 -0.486778 -0.873254** -0.512076*

[ 0.04553] [-0.90885] [ 0.09372] [-0.61896] [-0.58973] [-2.03963] [-1.60876]

D(lnPO(-1)) -1.263397* 0.065606 -0.261088 3.709406 3.252647 -5.394827 -1.820633

[-1.64247] [ 0.25960] [-0.32795] [ 0.32529] [ 0.51810] [-0.89653] [-0.38619]

D(lnPO(-2)) -0.547236 -0.001507 1.367009* 2.512136 1.600757 7.711938 3.713633

[-0.74783] [-0.00936] [ 1.86334] [ 0.21063] [ 0.24211] [ 1.28186] [ 0.64641]

D(lnRP(-1)) 11.94895 -1.463862** -0.004026 -0.073429 -0.387423 0.203022 -0.402430

[ 1.58516] [-2.28923] [-0.02315] [-0.15829] [-0.71947] [ 0.67736] [-1.12213]

D(lnRP(-2)) 12.40594 0.077742 0.088525 0.198464 0.577519 0.331117 0.599232*

[ 0.91885] [ 0.11450] [ 0.59344] [ 0.45751] [ 1.05318] [ 1.55740] [ 1.75046]

D(lnRER(-1)) -0.321530 -0.046788 2.305174 -0.141628 0.068781 1.239615* -0.793095

[-0.29249] [-0.07120] [ 0.70197] [-0.36247] [ 0.34376] [ 1.65458] [-0.99916]

D(lnRER(-2)) 0.553619 -0.219445 2.964084 -0.315900 0.181220 2.516507** 0.623563

[ 0.50009] [-0.32788] [ 0.88631] [-0.80100] [ 0.99065] [ 2.65153] [ 0.71973]

D(SDR(-1)) 0.007743 -0.248730 0.721391 0.100910 0.200690 0.061303* -0.090122

[ 0.01717] [-0.44762] [ 1.32857] [ 1.14271] [ 0.89539] [ 1.65205] [-1.15888]

D(SDR(-2)) -0.292970 0.143460 1.109990** 0.023297 0.054513 0.043176 0.031681

[-0.70150] [ 0.26668] [ 2.28353] [ 0.29196] [ 0.24468] [ 1.02261] [ 0.40065]

C 0.077968 0.061153 0.028771 -0.051234 -0.067775 0.030638 0.009392

[ 1.50114] [ 0.83523] [ 1.44193] [-0.53762] [-0.53545] [ 0.29972] [ 0.11661]

R-squared 0.427726 0.548354 0.893671 0.861416 0.633095 0.814481 0.922119

Adj. R-squared -0.009895 0.202978 0.787342 0.755440 0.352520 0.672614 0.862564

Sum sq. resids 0.841690 2.487204 0.118490 0.946312 1.609321 0.316707 0.341876

S.E. equation 0.222511 0.382500 0.088878 0.235935 0.307678 0.136491 0.141811

F-statistic 0.977390 1.587699 8.404763 8.128403 2.256423 5.741155 15.48330

Log likelihood 11.91103 -4.883262 42.30001 10.09504 1.864616 27.06128 25.87595

Akaike AIC 0.134773 1.218275 -1.696775 0.251933 0.782928 -0.842663 -0.766191

Schwarz SC 0.782380 1.865882 -0.956652 0.899540 1.430535 -0.195056 -0.118583

Mean dependent 0.019403 0.014946 0.027269 0.014747 -0.013304 0.012481 0.021047

S.D. dependent 0.221418 0.428446 0.192733 0.477090 0.382370 0.238547 0.382525

Figures in parenthesis are t-statistics (** = Sig. at 95%, *= Sig. at 90%).



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The percent levels are 2.0 and 1.6 respectively.

The results imply that when D(lnRER(-1)) of Thailand with the America

dollar increase by 1% then American tourist spending on goods and services in

Thailand increases by 1.23%. The results of short-term dynamics indicate that

D(lnRER(-2)) has a positive impact on international tourist spending on goodsand services in Thailand. The results imply that when D(lnRER(-2)) ofThailand with the America dollar increase by 1% then American tourist

spending on goods and services in Thailand increase by 2.51%. And finally, the

results of short-term dynamics indicate that D(SDR(-1)) has a positive impact

on international tourist spending on goods and services in Thailand. The resultsimply that when the D(SDR(-1)) of Thailand with America dollar increases by

1% then American spending on goods and services in Thailand increases by

0.06%. Furthermore, the results of short-term dynamics indicate that D(SDR(-

2)) has a positive impact on international tourist spending on goods and

services in Thailand. The results imply that when D(SDR(-1)) of Thailand withthe pound sterling increases by 1% then English tourist spending on goods and

services in Thailand increases by 1.10%.

Table 1.6 : Results of the short-term dynamics in international tourism demand

of Thailand base on VECM model (residual based diagnostic tests

onVECM model).

From : computed

Diagnostic Test Malaysia China England Germany France America Canada

Autocorrelation

TestLM(12)

(P-value)

40.47(0.27)

35.13(0.50)

35.81(0.92)

40.37(0.28)

37.63(0.39)

38.33(0.36)

33.42(0.56)

Normality TestJB -Test

(P-value)3.04

(0.21)3.63

(0.16)3.38

(0.18)2.45

(0.24)2.70

(0.25)3.03

(0.21)3.26

(0.19)

HeteroWhite Test

(P-value)554.56(0.39)

542.50(0.53)

- 552.38(0.41)

555.03(0.38)

554.25(0.39)

550.9(0.43)

Furthermore this paper applied a number of diagnostic tests to the error

correction model (See table 1.6). The models pass the Jarque-Bera normalitytest, suggesting that the errors are normally distributed. There is no evidence of

autocorrelation in the disturbance of the error term (See value of L.M.-test in

the same table). The RESET test indicates that the every model is correctlyspecified. The White-test suggest that the error is homoskedastic (except

England).



29/34

6. Conclusions and policy recommendationsThis paper was motivated by the need for an empirical analysis of the

determinants of international tourist expenditure in Thailand from its seven

main source markets, Malaysia, China, England, Germany, France, America

and Canada. The cointegration techniques based on Johansen and Juselius

(1990) were used for an empirical analysis of determinants of international

tourist expenditure for the long-run in Thailand. And the Vector Error

Correction Mechanism (VECM) model was used for an empirical analysis of

determinants of international tourist expenditure for short-term dynamics in

Thailand. The economic variables used in this research were: expenditure of

international tourists to Thailand, the numbers of international tourists to

Thailand, the GDP of the countries of origin of international tourists, the world

price of kerosene-type jet fuel, the relative cost of Thailand with the cost of the

countries of origin of international tourists, and both the real exchange rate and

exchange rate risk of Thailand and the countries of origin of international

tourists to Thailand. Furthermore, this paper determined the order of integration

of the variables by six standard method tests for the unit root. Namely, theADF-Test (1979), PP-Test (1987,1988), KPSS-Test (1992), DF-GLS Test

(1996), ERS Point Optimal Test and Ng and Perron (2001).

There are four conclusions and recommendations that emerge from the

empirical analysis based on Johansen and Juselius (1990) (coefficients() fromVECM model). First, a 1% increase in the number of international touristarrivals in Thailand (lag one period) in the long-run in the main source markets

of France and Canada leads to an increase in international tourist spending on

goods and services in Thailand by 2.27% and 18.35%, respectively. Otherwise

a 1% increase in the number of international tourist arrivals in Thailand (lag

one period) in the long-run in main source markets, Malaysia, China, England

and German leads to a decrease in international tourist spending on goods and

services in Thailand by 2.04%, 1.07%, 5.07% and 2.01%, respectively. The

long-run results imply that tourists from both France and Canada like the goods

and services of Thailand much more than tourists from Malaysia, China,

England and Germany. If this can be generalized for future years, it suggests

the policy makers of Thailand should help producers in Thailand develop goods

and services for the satisfaction of international tourists, especially tourists

from Malaysia, China, England and Germany. Second, a 1% increase in income(GDPt-1) in the long-run in the main source markets of China, England,

Germany and America leads to an increase in international tourist spending on

goods and services in Thailand by 0.97%, 6.45%, 1.96% and 1.80%,

respectively. Otherwise a 1 % increase in income (GDP t-1) in the long-run in

Canada leads to an decrease in international tourist spending on goods and

services in Thailand by 77.04%. If this can be generalized for future years, then

it suggests that the policy makers in Thailand should continue the development

of the Thai tourism industry. Third, a 1% increase in the cost of transportation

(world price of jet fuel) in the long-run leads to a reduction of spending on

goods and services in Thailand by tourists from the main source markets(Malaysia, China, England, Germany, France and America) by 0.97%, 0.26%,



30/34

0.53%, 0.10%, 0.35% and 0.28% respectively. Otherwise a 1% increase in

transportation costs (world price of jet fuel) in the long-run leads to an increase

in spending on goods and services in Thailand by Canadian tourist by 2.43%. If

this can be generalized for future years, then it suggests that the policy makers

of Thailand should increase support for international low cost airlines or reduce

the cost for international airlines arriving in Thailand because the Thai

government can not control the price of jet fuel in future. Fourth, in the long-

run the exchange rate risk is an important determiner of international tourist

spending on goods and services and a 1% increase in the exchange rate risk of

Thailand against the currency of the major tourist markets of Malaysia, China,

England, Germany and Canada leads to a decrease in international tourist

spending on a goods and services in Thailand by 1.40%, 0.46%, 0.09%, 0.17%

and 2.49% respectively. This results is consistent with economic theory and it

suggests that the Reserve Bank of Thailand should be careful when using any

policy that impacts on Thai currency because when the Thai currency is very

both strong and risk, it not only negatively impacts on export goods and

services (Anderson and Garcia (1989), Pick (1990), Chukiat (2003)) but it alsodecreases international tourist spending on goods and services in Thailand.



31/34

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