Forecasting foreign tourists in Thailand by economic condition for tourism index

Chia sẻ: Nguyễn Thảo | Ngày: | Loại File: PDF | Số trang:9

Thêm vào BST

Báo xấu

35
lượt xem 4
download

Download Vui lòng tải xuống để xem tài liệu đầy đủ

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.

Chủ đề:

Bình luận(0) Đăng nhập để gửi bình luận!

Lưu

Nội dung Text: Forecasting foreign tourists in Thailand by economic condition for tourism index

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 http://www.iaeme.com/IJMET/index.asp 144 editor@iaeme.com
Bundit Chaivichayachat 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: Pi - Min (Pi ) Yi  (1) Max (Pi ) - Min (Pi ) where Pi is the positive economic variable on tourism including YM, RT, TB, NE, HG, HS, and PO, Yi 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 Max (Ni ) - Ni Yi  Max (Ni ) - Min (Ni ) (2) Where N i is the negative economic variable on tourism including PT and PS. The normalized variable values, both Yi and Vi , 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 http://www.iaeme.com/IJMET/index.asp 145 editor@iaeme.com
Forecasting Foreign Tourists in Thailand by Economic Condition for Tourism Index 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  Yi Is  i  1 k (3) where Is 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  2FTN   2Y 1 (L) 2   2Y 2 (L) 2  ...   2Y k (L) 2 1 2 k (4) where  2FTN is variance of the forecast error for the number of foreign tourists,  2Y is the j th variance of the j 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 (B)FTN t  (B)ECTt-b  t (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 Bk FTNt  FTNt-k , t is r whitenoise residual process, (B)  1 - 1B - 2B2 -...- r Br , 1 (B)t  1 (B) t 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, http://www.iaeme.com/IJMET/index.asp 146 editor@iaeme.com
Bundit Chaivichayachat 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 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 Table 2: Weight of Economic Factor http://www.iaeme.com/IJMET/index.asp 147 editor@iaeme.com
Forecasting Foreign Tourists in Thailand by 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 Total 1.0000 1.0000 1.0000 Figure 1: Economic Condition for Tourism Index and Number of Foreign Tourist (normalized) Table 3: Economic Condition for Tourism Index http://www.iaeme.com/IJMET/index.asp 148 editor@iaeme.com
Bundit Chaivichayachat 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 http://www.iaeme.com/IJMET/index.asp 149 editor@iaeme.com
Forecasting Foreign Tourists in Thailand by Economic Condition for Tourism Index 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. http://www.iaeme.com/IJMET/index.asp 150 editor@iaeme.com
Bundit Chaivichayachat 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 http://www.iaeme.com/IJMET/index.asp 151 editor@iaeme.com
Forecasting Foreign Tourists in Thailand by Economic Condition for Tourism Index [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). http://www.iaeme.com/IJMET/index.asp 152 editor@iaeme.com