Tourism sector in Thailand has been promoted as a key sector to create economic expansion since the early of 2010s. The increasing in demand for tourism in Thailand induces a significant final demand in tourism related sectors. In order to prepare for the growing in tourism sector, the number of visitors should be forecasted. This paper set up a new composite index called economic condition for tourism (ECT) index. This index summarizes the information of the macroeconomic variables which effect the demand for tourism in Thailand. The methods of composite index are implemented. There three weighted average methods that were used: simple average, variance decomposition and factor important. Then, the ECT index can be used as a leading indicator for number of foreign tourists. The ECT index also act as the important factor forecast the number of foreign tourists. Tourism price in Thailand, tourism price in AEC and crime rate are the most important economic factor to determine the ECT index and for the forecasting the number of foreign tourists. The results emphasis that to support the tourism promoting, price stability policy and crime reducing policy should be considered.

1. http://www.iaeme.com/IJMET/index.asp 144 editor@iaeme.com
International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 03, March 2019, pp. 144-152. Article ID: IJMET_10_03_014
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
FORECASTING FOREIGN TOURISTS IN
THAILAND BY ECONOMIC CONDITION FOR
TOURISM INDEX
Bundit Chaivichayachat
Department of Economics, Faculty of Economics, Kasetsart University
Bangkok, Thailand
ABSTRACT
Tourism sector in Thailand has been promoted as a key sector to create economic
expansion since the early of 2010s. The increasing in demand for tourism in Thailand
induces a significant final demand in tourism related sectors. In order to prepare for
the growing in tourism sector, the number of visitors should be forecasted. This paper
set up a new composite index called economic condition for tourism (ECT) index. This
index summarizes the information of the macroeconomic variables which effect the
demand for tourism in Thailand. The methods of composite index are implemented.
There three weighted average methods that were used: simple average, variance
decomposition and factor important. Then, the ECT index can be used as a leading
indicator for number of foreign tourists. The ECT index also act as the important
factor forecast the number of foreign tourists. Tourism price in Thailand, tourism
price in AEC and crime rate are the most important economic factor to determine the
ECT index and for the forecasting the number of foreign tourists. The results emphasis
that to support the tourism promoting, price stability policy and crime reducing policy
should be considered.
Key words: Tourists, Leading Indicator, Transfer Function,
Cite this Article: Bundit Chaivichayachat, Forecasting Foreign Tourists in Thailand
by Economic Condition for Tourism Index, International Journal of Mechanical
Engineering and Technology, 10(3), 2019, pp. 144-152.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=3
1. INTRODUCTION
In Thailand has been promoted as a key sector to create economic expansion since the early of
2010s. Therefore, tourism promoting policies both in form of qualitative and quantitative
measures have been implemented. As a result, the demand of Thai‟s tourism increases
continuously. Not only the policy to promoting the tourism sector, the demand for Thai
Tourism also response to the macroeconomic variables including exchange rate, inflation,
tourist‟s income, securities and political condition (Kara et al., 2005; Alvarez, 2007; Allen

2. Bundit Chaivichayachat
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and Yop, 2009; Onder et al., 2009; Monebi and Rahim, 2010; Song and Wei, 2010;
HanafionHarun and Jamaluddin, 2011; Ibranim, 2011; Skuflic and Stokovic, 2011; Betonio,
2013; Altindag, 2013; Bentum-Ennin, 2014; Deluna and Jeon, 2014; Laframboise et al., 2014;
Moorthy, 2014; Bundit, 2017; and Bundit, 2018). The increasing in demand for tourism in
Thailand induces a significant final demand in tourism related sectors. Finally, the linkages
between the tourism sector and the tourism related sectors initiate an economic expansion for
Thai economy. In order to prepare for the growing in tourism sector, the number of visitors
should be forecasted. This paper aims to forecast the number of foreign tourists by used the
economic condition for tourism index (ECT) and the transfer function. The results can be used
to setup the policy to support the expansion of the tourism sector in Thailand
2. METHODOLOGY
The number of foreign tourists, the research method are the composite index and transfer
function. First, the composite index named economic condition for tourism index (ECT) will
be calculated. This index summarized all economic condition that effect the demand for
tourism in Thailand by foreign tourists. The increasing ECT will be followed by the
increasing in tourism demand and tourism revenues. In contrast, the declining in ECT will be
followed by the slowdown in tourism demand and tourism revenues. Then, after the ECT
index was calculated, the transfer function is employed to estimate a linear equation to
forecast the number of foreign tourists. For the first step, the set of economic variables the
determined the demand for tourism will be defined following the demand theory, maximized
behavior and the recent empirical works. The economic variables that determined demand for
tourism by foreign tourists are tourist‟s income (million Baht: YM), tourism price (consumer
price index in Thailand: PT), exchange rate (Bath: US dollar: NE), market share of retail trade
sector (ratio of GDP in retail trade to total GDP: RT), tourism promoting policy (million baht:
TB), number of hotels and guest houses (number: HG), number of hospital (hospital approved
by Ministry of Health: HS), crime rate (time: CR), and economic, tourism price in AEC
(average consumer price index in AEC countries: PO) and political condition (dummy
variable given 1 for instability occurred and 0 for another: PS). These economic variables are
normalized to cancel the difference in measurement unit. Following UNDP (2013), the
economic variables which generate the positive effect on demand for Thai tourism can be
normalized as:
i i
i
i i
P - Min (P )
Y
Max (P ) - Min (P )
 (1)
where iP is the positive economic variable on tourism including YM, RT, TB, NE, HG,
HS, and PO, iY is normalized of positive economic variables. The normalized variable value
is lie between 0 and 1.
For the negative factors, they can be normalized as
i i
i
i i
Max (N ) - N
Y
Max (N ) - Min (N )

(2)
Where iN is the negative economic variable on tourism including PT and PS.
The normalized variable values, both iY and iV , are lie between 0 and 1.
Once the normalized variables are calculated, the method to setup a composite index is
implemented for calculate the ECT index. There are many methods for setup the composite
index. For example, WTTC (2013) employed simple average. Gooroochurn and Sugiyarto
(2005) and Fernandez and Rivero (2009) used factor analysis to calculate weight of each

3. Forecasting Foreign Tourists in Thailand by Economic Condition for Tourism Index
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economic variable. Freedman (1994, 1996), Lack (2003), Goodhart and Hofmann (2001),
Chong (2014) and Chang, Hsu and McAleer (2014) estimated weight of each normalized
factor by calculated variance decomposition from vector autoregressive (VAR). This paper
will used for 3 methods to calculate the ECT index. The first method is the simple average
method. The index can be setup as
k
i
i 1
s
Y
I
k


(3)
where sI is the economic condition for tourism index by simple average, i = 1, 2, …, k
and k is the number of normalized economic factor.
Second, ECT index will be calculated by estimated the variance decomposition in vector
autoregressive (VAR). It is the same methods as the composite index for financial condition
index (FCI) and monetary condition index (MCI). The variance decomposition equation
presented as
2 2 2 2 2 2 2
FTN 1 2 kY Y Y1 2 k
(L) (L) ... (L)          
(4)
where 2
FTN is variance of the forecast error for the number of foreign tourists, 2
Yj
 is the
variance of the jth
economic factors and j(L) is lag operator in the impulse response function
from vector autoregressive.
For the third way, this paper proposes new method for the calculation for the weight of
economic factors based on artificial neural network (ANN) for forecast the number of foreign
tourists. The factor important measures the importance of ith factor on the forecasting of
foreign tourist. The summation of factor important overall economic factors equal one. (see
Bundit,2015 and Bundit,2017).
Once the ECT index is calculated, the next step is the estimation of the transfer function.
(Kulendran and Witt, 2003 and Santos and Macedo, 2008). The basic idea of the transfer
function is autoregressive integrated moving average (ARIMA) which including 2 parts, AR
term and MA term. In the transfer function, the ECT is invited explicitly to play a role for
forecast tourist number. Therefore, there are three parts; moving average (MA),
autoregressive (AR) and the ECT index. The transfer function can be presented as
t t-b t(B)FTN (B)ECT     (5)
where FTN is output series or number of foreign tourists, ECT is input series or the
economic condition for tourism index, B is lag operator where k
t t-kB FTN FTN , t is r
whitenoise residual process, 2 r
1 2 r(B) 1 - B - B -...- B     , 1 t(B)  1 t(B)   and t is
whitenoise term.
3. RESULTS AND DICUSSION
Thailand, National Statistic Office of the National Economic and Social Development
Board, were arranged. Then, the normalized economic factors which used for organized the
economic condition for tourism index shown in Table 1. Then, the weight for each factor
which estimated by simple average, variance decomposition and factor important are
presented in Table 2. Based on Table 2, the economic condition for tourism index following 3
methods are shown in Figure 1 and Table 3. The results indicate that the ECT index by 3
methods move at the same pattern. In comparing with normalized number of foreign tourists,

4. Bundit Chaivichayachat
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the ECT index move very close to the normalized number of foreign tourists especially in
2013 and 2014. The correlation between the ECT by simple average, variance decomposition
and factor important are 0.931, 0.891 and 0.931 respectively. Moreover, before employ the
ECT as a determinant for forecasting number of foreign tourist in transfer function, the
Granger causality test was performed. The results by Granger causality represent the causality
between normalized number of foreign tourists and the ECT index. The Granger causality test
shown in Table 4. Along the null hypotheses, only one null hypothesis can be rejected. The F
statistic indicates the null hypothesis, ECT_VDC does not Granger causality, was rejected
with statistical significant. Only the economic condition for tourism index by variance causes
normalized number of foreign tourists with statistically significance. Therefore, in the next
step, the ECT index by variance decomposition will enter to the transfer function for
forecasting number of foreign tourists. Moreover, the weight of variance decomposition
shown that tourism cost, tourism cost in AEC and crime rate are the most important
macroeconomic variables to determine demand for Thai‟s tourism.
Next, the ECT index weighted by variance decomposition will enter to the transfer function a
written in equation (5). At first, we proposed lag length equal to 4 for quarterly data for all term in the
transfer function (autoregressive, moving average and economic condition index) and developed the
estimation results by General-to-Specific method. Finally, the best estimated transfer function
presented in Table 5. The ECT index lag 1 and lag 3 determine number of foreign tourists with
statistically significance. Then, the ECT index weighted by variance decomposition can be used as
leading indicator for number of foreign tourists. (Kulendran and Witt, 2003; Santos and Macedo,
2008; Fernandez and Rivero, 2009 and Chang, Hsu and McAleer, 2014).
Table 1: Normalized Economic Factors
Table 2: Weight of Economic Factor
YM NE PT RT PO TB HG HS CR PS
2008 Q1 0.0083 0.0136 0.0904 0.0788 0.0178 0.0002 0.0000 0.0557 0.0000 0.0360
Q2 0.0087 0.0130 0.1190 0.1050 0.0200 0.0000 0.0011 0.0522 0.0007 0.0360
Q3 0.0085 0.0117 0.1162 0.1174 0.0210 0.0002 0.0023 0.0493 0.0011 0.0360
Q4 0.0075 0.0101 0.0570 0.1198 0.0160 0.0009 0.0035 0.0470 0.0003 0.0360
2009 Q1 0.0000 0.0078 0.0297 0.1035 0.0075 0.0045 0.0048 0.0431 0.0012 0.0360
Q2 0.0000 0.0157 0.0000 0.1039 0.0029 0.0052 0.0061 0.0428 0.0009 0.0360
Q3 0.0017 0.0261 0.0071 0.0942 0.0000 0.0054 0.0075 0.0440 0.0011 0.0360
Q4 0.0050 0.0274 0.0546 0.0823 0.0027 0.0052 0.0089 0.0467 0.0019 0.0360
2010 Q1 0.0152 0.0365 0.0753 0.0762 0.0063 0.0001 0.0104 0.0572 0.0008 0.0360
Q2 0.0197 0.0263 0.0694 0.0976 0.0073 0.0008 0.0119 0.0602 0.0010 0.0000
Q3 0.0237 0.0222 0.0700 0.1096 0.0078 0.0027 0.0135 0.0621 0.0028 0.0000
Q4 0.0273 0.0219 0.0658 0.1078 0.0085 0.0059 0.0151 0.0629 0.0034 0.0360
2011 Q1 0.0297 0.0202 0.0672 0.0995 0.0094 0.0149 0.0168 0.0660 0.0037 0.0360
Q2 0.0326 0.0000 0.0797 0.1341 0.0118 0.0190 0.0185 0.0633 0.0018 0.0000
Q3 0.0354 0.0274 0.0801 0.1370 0.0109 0.0227 0.0203 0.0581 0.0035 0.0000
Q4 0.0381 0.0271 0.0782 0.1202 0.0096 0.0259 0.0221 0.0504 0.0076 0.0000
2012 Q1 0.0400 0.0328 0.0715 0.0000 0.0073 0.0275 0.0240 0.0303 0.0043 0.0000
Q2 0.0426 0.0510 0.0614 0.0131 0.0042 0.0303 0.0259 0.0218 0.0054 0.0360
Q3 0.0452 0.0570 0.0662 0.0149 0.0042 0.0331 0.0279 0.0148 0.0050 0.0360
Q4 0.0480 0.0423 0.0696 0.0106 0.0056 0.0360 0.0299 0.0094 0.0068 0.0000
2013 Q1 0.0510 0.0386 0.0681 0.0087 0.0060 0.0394 0.0320 0.0002 0.0073 0.0000
Q2 0.0538 0.0317 0.0590 0.0238 0.0063 0.0421 0.0341 0.0000 0.0074 0.0000
Q3 0.0565 0.0316 0.0516 0.0314 0.0071 0.0447 0.0363 0.0034 0.0080 0.0360
Q4 0.0591 0.0385 0.0517 0.0223 0.0064 0.0471 0.0386 0.0105 0.0061 0.0360
2014 Q1 0.0620 0.0474 0.0554 0.0090 0.0062 0.0538 0.0408 0.0368 0.0045 0.0000
Q2 0.0645 0.0316 0.0609 0.0249 0.0059 0.0540 0.0432 0.0450 0.0053 0.0000
Q3 0.0668 0.0385 0.0554 0.0343 0.0049 0.0522 0.0456 0.0505 0.0048 0.0000
Q4 0.0690 0.0474 0.0451 0.0324 0.0045 0.0485 0.0480 0.0535 0.0040 0.0000

5. Forecasting Foreign Tourists in Thailand by Economic Condition for Tourism Index
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Figure 1: Economic Condition for Tourism Index and Number of Foreign Tourist (normalized)
Table 3: Economic Condition for Tourism Index
Simple Average Variance Decomposition Factor Important
Tourist's Income YM 0.1000 0.0862 0.1070
Exchange Rate NE 0.1000 0.0718 0.0970
Tourism Price PT 0.1000 0.2718 0.1570
Size of Retail Trade Sector RT 0.1000 0.1077 0.1770
Tourism Price in AEC PO 0.1000 0.2030 0.0570
Tourism Promoting Budget TB 0.1000 0.0057 0.0870
Hotel and Guest House HG 0.1000 0.0063 0.0870
Hospital HS 0.1000 0.0444 0.1070
Crime Rate CR 0.1000 0.1627 0.0470
Economic and Political Instablity PS 0.1000 0.0404 0.0770
1.0000 1.0000 1.0000Total

6. Bundit Chaivichayachat
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Table 4: Pairwise Granger Causality Test
Table 5: Transfer Function for Forecasting Number of Foreign Tourists (FTN)
Table 6: Number of Foreign Tourists: Actual and Fitted

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Figure 2: Number of Foreign Tourists: Actual and Fitted
The estimated transfer function was applied to calculated the fitted value of the number of
foreign tourists during 2008 to 2014 to evaluate the forecasting performance or ex pose
forecast. The results are in Figure 2 and Table 6. Theil‟s inequality coefficient is calculated,
and it equals to 0.0585. The meaning is that the difference between the actual and fitted value
records only 5.85 percent. The estimated transfer function is ready to do the ex ante forecast.
By employing the estimated transfer function, the number of foreign tourists in the first
quarter of 2015 is 7,735,880 persons. For the actual, the number of foreign tourists in this
period is 7,829,153 persons.

8. Bundit Chaivichayachat
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4. CONCLUSION
This paper set up a new composite index called economic condition for tourism (ECT) index.
This index summarizes the information of the macroeconomic variables which effect the
demand for tourism in Thailand. The methods of composite index are implemented. There
three weighted average methods that were used: simple average, variance decomposition and
factor important. Among these three methods, the ECT index by variance decomposition can
be used as one period leading indicator for demand for Thai tourism. Then, the ECT index can
be used as a leading indicator for number of foreign tourists. The ECT index also act as the
important factor forecast the number of foreign tourists. In order to forecast the number of
foreign tourists, it is necessary to focus on the macroeconomic factors. Tourism price in
Thailand, tourism price in AEC and crime rate are the most important economic factor to
determine the ECT index and for the forecasting the number of foreign tourists. The results
emphasis that to support the tourism promoting, price stability policy and crime reducing
policy should be considered.
REFERENCES
[1] Allen, David, Yap, Ghialy, and Shareef Riaz. 2009. Modeling interstate tourism demand
in Australia: A cointegration approach. Mathematics and Computers in Simulation 79(9):
2733-2740, http://dx.doi.org/ 10.1016/j.matcom.2008.10.006
[2] Betonio, M. 2013. Tourism in Asia: Determinants of Tourist arrivals in Asia Countries.
De La Salle University.
[3] Bentum-Ennin. 2014. “Modelling international tourism demand in Ghana,” Global
Business and Economics Research Journal. Vol. 3 (12): 1 – 22
[4] Chang, C.-L., H.-K. Hsu and M. McAleer 2014, The impact of China on stock returns and
volatility in the Taiwan tourism industry, North American Journal of Economics and
Finance, 29C, 381-401.
[5] Chang, C.-L. Modelling a latent daily Tourism Financial Conditions Index. Int. Rev.
Econ. Finance. 2015, 40, 113–126.
[6] Deluna, R. Jr. and Jeon, N. 2014. Determinants of International Tourism Demand for the
Philippines: An Augmented Gravity Model Approach. MPRA Paper No. 55294
[7] Fernandez, J. I. P. and Rivero, M. S. 2009. “Measuring tourism sustainability: Proposal
for a composite index”. Tourism Economics, 15(2), pp. 277 -296.
[8] Freedman, C. 1994. “The Use of Indicators and of the Monetary Conditions Index in
Canada” International Monetary Fund: Washington, DC, USA, 1994.
[9] Freedman, C. The role of monetary conditions and the Monetary Conditions Index in the
conduct of policy. In The Transmission of Monetary Policy in Canada; Bank of Canada:
Ottawa, ON, Canada, 1996; pp. 81–86.
[10] Goodhart, C.; Hofmann, B. Asset prices, financial conditions, and the transmission of
monetary policy. In Proceedings of the Conference on Asset Prices, Exchange Rates and
Monetary Policy, 2–3 March 2001; Federal Reserve Bank of San Francisco and Stanford
Institute for Economic Policy Research: San Francisco, CA, USA, 2001.
[11] Gooroochurn, N., & Sugiyarto, G. 2005. Competitiveness indicators in the travel and
tourism industry. Tourism Economics, 25-43.
[12] Hanafiah, M.H., M.F. Harun and Jamaluddin M.R., 2010. Bilateral Trade and Tourism
Demand. World Applied Sciences Journal, 10 (Special Issue of Tourism & Hospitality),
110-114.
[13] Ibrahim, M. A. M. (2011). The Determinants of International Tourism Demand for Egypt:
Panel Data Evidence. European Journal of Economics, Finance and Administrative
Sciences, 30: 50-58

9. Forecasting Foreign Tourists in Thailand by Economic Condition for Tourism Index
http://www.iaeme.com/IJMET/index.asp 152 editor@iaeme.com
[14] Kara, A., Lonial, S., Tarim, M., Zaim, S. 2005. A paradox of service quality in Turkey:
the seemingly contradictory relative importance of tangible and intangible determinants of
service quality. European Business Review, 17(1), 5-20.
[15] Kulendran, N., and Witt, S. F. 2003a) „Forecasting the demand for international business
tourism‟, Journal of Travel Research, 41, 265-271.
[16] Kulendran, N., and Witt, S. F. 2003b) „Leading indicator tourism forecasts‟, Tourism
Management, 24, 503-510.
[17] Lack, C.P. A Financial Conditions Index for Switzerland. In Monetary Policy in a
Changing Environment; Bank for International Settlements: Basel, Switzerland, 2003;
Volume 19, pp. 398–413.
[18] Laframboise, Nicole, Nkunde Mwase, Joonkyu Park, and Yingke Zhou. 2014, “Revisiting
Tourism Flows to the Caribbean: What is Driving Arrivals?”. IMF Working Paper 14/229.
[19] Moorthy, R. 2014. An Empirical Analysis of Demand Factors For Malaysian Tourism
Sector Using Stochastic Methods. Review of Integrative Business & Economics Research,
Vol 3(2), 255-267.
[20] Önder, A., Candemir, A., & Kumral, N. 2009. An empirical analysis of the determinants
of international tourism demand: The case of Izmir. European Planning Studies, 17(10),
1525.
[21] Škuflić, L. and Štoković, I. 2011, “Demand Function for Croatian Tourist Product: A
Panel Data Approach”, Modern Economy, No. 2, pp. 49-53.
[22] World Travel & Tourism Council, 2013. [On-line]. Available at: http://www.wttc.org
(Accessed 20 March, 2013).