Modelling and Forecasting Tourist Arrivals to Cambodia: An Application of ARIMA-GARCH Approach

Authors: 
Theara Chhorn, Chukiat Chaiboonsri
DOI Number: 
doi.org/10.31039/jomeino.2018.2.2.1
Abstract: 
The paper aims at estimating and forecasting international tourist arrivals to Cambodia during the time interval of 2000m1 to 2017m7, covering 209 of monthly observations. To find out factors affecting tourist arrivals, simple OLS and 2SLS with instrument variable regression are applied, on the one hand. On the other hand, several time series models of ARIMA (p, d, q), GARCH (s, r) and the hybrid of ARIMA(p, d, q)-GARCH(s, r) are employed to forecast tourist arrivals in line with AIC and BIC in selecting the best modified models. The empirical results primarily reveal that tourist arrivals are affected by exogenous factor, say exchange rate, dummy factors such as the AEC, global finical crisis, national election and Cambodia’s e-Visa. With regard to forecasting stage, the result indicates that tourist arrivals are shocked by time trend in the past period, say time (t-1). The trend is furthermore reduced due to the time lags, say time (t-2, t-3) as shown in the parameter coefficients of AR. GARCH (1, 1) model suggests that the short run persistence of shocks lies in the gap of 0.04 whereas the long run persistence lies in the gap of 0.94. Additionally, AIC and BIC propose that ARIMA(3, 1, 4) and the hybrid of ARIMA(3, 1, 4)-GARCH (1, 1) are the best model to predict the future value of tourist arrivals. The RMSE and U index obtained from measurement predictive accuracy reveal that long run 1-step ahead forecasting of 2013m12 to 2017m7 is produced the smallest predictive error amongst the others. Thus, it has more predictive power to apply long term ex-ante forecasting.
Keywords: 
Point Forecasting Interval, out of Sample Forecasting, ARIMA (p, d, q)- GARCH (s, r) Model, Exchange rate and Dummy Factors, Tourist Arrivals, Cambodia
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Saayman, A., & Botha, I. (2017). Non-linear models for tourism demand forecasting. Tourism Economics, 23(3), 594-613. https://doi.org/10.5367%2Fte.2015.0532
Baum, C. F. (2015). ARCH and MGARCH models. Boston College.
Bloomfield, P. (2009). Modeling the variance of a time series (Symposium presentation). Ben Kedem Symposium, Department of Statistics, North Carolina State University.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
Box, G. E., & Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American statistical Association, 65(332), 1509-1526.
Box, G. E., P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Holden-Day, San Francisco.
Chang, C. L., & McAleer, M. (2009). Daily tourist arrivals, exchange rates and volatility for Korea and Taiwan. Exchange Rates and Volatility for Korea and Taiwan, 25, 241-267. https://dx.doi.org/10.2139/ssrn.1504651
Chen, K. Y. (2011). Combining linear and nonlinear model in forecasting tourism demand. Expert Systems with Applications, 38(8), 10368-10376. https://doi.org/10.1016/j.eswa.2011.02.049
Chen, M. Y. (2013). Time series analysis: Conditional volatility models. Department of Finance, National Chung Hsing University.
Chia-Lin Chang, Michael McAleer and Dan Slottje. 2009. "Modelling International Tourist Arrivals and Volatility: An Application to Taiwan." Department of Applied Economics, National Chung Hsing University, Taiwan.
Chang, C. L., McAleer, M., &Slottje, D. J. (2009). Modelling international tourist arrivals and volatility: An application to Taiwan. In D. J. Slottje (Ed.), Quantifying consumer preferences-contributions to economic analysis Vol. 288 (pp. 299-315). Emerald Group Publishing Limited, Bingley. https://doi.org/10.1108/S0573-8555(2009)0000288014
Chang, C. L., Khamkaew, T., Tansuchat, R., & McAleer, M. (2011). Interdependence of international tourism demand and volatility in leading ASEAN destinations. Tourism Economics, 17(3), 481-507. https://doi.org/10.5367%2Fte.2011.0046
Tendai, M., & Chikobvu, D. (2017). Modelling international tourist arrivals and volatility to the Victoria Falls Rainforest, Zimbabwe: Application of the GARCH family of models. African Journal of Hospitality, Tourism and Leisure, 6(4), 1-16.
Chu, F. L. (2009). Forecasting tourism demand with ARMA-based methods. Tourism Management, 30(5), 740-751. https://doi.org/10.1016/j.tourman.2008.10.016
Crouch, G. I. (1993). Currency exchange rates and the demand for international tourism. Journal of Tourism Studies, 4(2), 45-53.
Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: journal of the Econometric Society, 49(4), 1057-1072. https://doi.org/1912517
Ferenstein, E., & Gasowski, M. (2004). Modelling stock returns with AR-GARCH processes. SORT-Statistics and Operations Research Transactions, 55-68.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 50(4), 987-1007. https://doi.org/1912773
Gang, J. (n.d.). Advanced STATA with time series data 1. Advanced time series using STATA 1. Session 5, Beihang University.
Agiomirgianakis, G., Serenis, D., & Tsounis, N. (2014). Exchange rate volatility and tourist flows into Turkey. Journal of Economic Integration, 29(4), 700-725. http://dx.doi.org/10.11130/jei.2014.29.4.700
My Joy Online. (2016, November 30). Impact upcoming elections on tourism and hospitality industry. My Joy Online. www.myjoyonline.com
Jánský, I., & Rippel, M. (2011). Value at risk forecasting with the ARMA-GARCH family of models in times of increased volatility. IES Working Paper, No. 27/2011, Institute of Economic Studies (IES), Charles University in Prague.
Kim, J., Lee, C. K., & Mjelde, J. W. (2018). Impact of economic policy on international tourism demand: the case of Abenomics. Current Issues in Tourism, 21(16), 1912-1929. https://doi.org/10.1080/13683500.2016.1198307
Perles-Ribes, J. F., Ramón-Rodríguez, A. B., Sevilla-Jiménez, M., & Rubia, A. (2016). The effects of economic crises on tourism success: An integrated model. Tourism Economics, 22(2), 417-447. https://doi.org/10.5367/te.2014.0428
Gul, K., Asik, N. A., & Gurbuz, A. K. (2014). The effect of global economic crisis on Turkish tourism demand and a review for the period 2003-2013. Journal of World Economic Research, 3(6-1), 22-32. https://doi.org/10.11648/j.jwer.s.2014030601.14
Kunst, R. M. (2012). Econometric forecasting. Institute for Advanced Studies Vienna, University of Vienna. http://homepage.univie.ac.at/robert.kunst/progpres.pdf.
Balli, F., & Jean Louis, R. (2015). Modelling the tourism receipt’s volatility. Applied Economics Letters, 22(2), 110-115. https://doi.org/10.1080/13504851.2014.929616
Falat, L., Stanikova, Z., Durisova, M., Holkova, B., & Potkanova, T. (2015). Application of neural network models in modelling economic time series with non-constant volatility. Procedia Economics and Finance, 34, 600-607. https://doi.org/10.1016/S2212-5671(15)01674-3
Mamula, M. (2015). Modelling and forecasting international tourism demand-evaluation of forecasting performance. International Journal of Business Administration, 6(3), 102-112. http://dx.doi.org/10.5430/ijba.v6n3p102
Shareef, R., & McAleer, M. (2005). Modelling international tourism demand and volatility in small island tourism economies. International Journal of Tourism Research, 7(6), 313-333. https://doi.org/10.1002/jtr.538
MoT. (2012). Tourism development strategic plan 2012-2020. Ministry of Tourism, Cambodia. http://www.tourismcambodia.org/images/mot/legal_documents/tourism_develo....
NCSS. (n.d.) The Box-Jenkins method. Chapter 470, NCSS Statistical Software.
Pathnadabutr, A. (2013, February 15). AEC, tourism drive investment trends. National Multi Media. http://www.nationmultimedia.com/business/AEC-tourism-driveinvestment-tre....
Leung, P., Lam, T., & Wong, S. (1996). Tourism development in Cambodia: An analysis of opportunities and barriers. Asia Pacific Journal of Tourism Research, 1(2), 27-33. https://doi.org/10.1080/10941669708721973
Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346. https://doi.org/10.1093/biomet/75.2.335
Ramos, C. M., & Rodrigues, P. M. (2014). Tourism demand modelling and forecasting: an overview. Revista de Turismo Contemporâneo, 2(2), 323-340.
Reider, R. (2009). Volatility forecasting I: GARCH models. New York, 1-16.
Andrawis, R. R., Atiya, A. F., & El-Shishiny, H. (2011). Combination of long term and short term forecasts, with application to tourism demand forecasting. International Journal of Forecasting, 27(3), 870-886. https://doi.org/10.1016/j.ijforecast.2010.05.019
Serenis, D., & Tsounis, N. (2014). The effects of exchange rate volatility on sectoral exports evidence from Sweden, UK, and Germany. International Journal of Computational Economics and Econometrics, 5(1), 71-107.
Kulendran, N., & Shan, J. (2002). Forecasting China's monthly inbound travel demand. Journal of Travel & Tourism Marketing, 13(1-2), 5-19. https://doi.org/10.1300/J073v13n01_02
Shira, D. (2016, March 15). Foreign exchange volatility in ASEAN: Assessing exposure & managing risk. ASEAN Briefing. https://www.aseanbriefing.com/news/5230/
Sjo, B. (2011). Estimation and Testing for ARCH and GARCH. Modelling the volatility of the Electrolux stock.
Song, H., Li, G., Witt, S. F., & Fei, B. (2010). Tourism demand modelling and forecasting: How should demand be measured?. Tourism Economics, 16(1), 63-81. https://doi.org/10.5367%2F000000010790872213
Hoti, S., León, C. J., & McAleer, M. (2004). International tourism demand and volatility models for the Canary Islands. International Congress on Environmental Modelling and Software. Article 113. https://scholarsarchive.byu.edu/iemssconference/2004/all/113
Ahmed, A. E. M., & Suliman, S. Z. (2011). Modeling stock market volatility using GARCH models evidence from Sudan. International Journal of Business and Social Science, 2(23), 114-128.
MoT. (2017). Tourism statistics. Statistics and Tourism Information Department, Ministry of Tourism, Phnom Penh, Cambodia. http://www.mot.gov.kh.
Steiner, C., Richter, T., Dörry, S., Neisen, V., Stephenson, M. L., Lemma, A. F., & Mitchell, J. G. (2012). Economic crisis, international tourism decline and its impact on the poor. Economic crisis, international tourism decline and its impact on the poor. World Tourism Organization (WTO) Publications.
Wang, T. (2016). Forecast of economic growth by time series and scenario planning method—A case study of Shenzhen. Modern Economy, 7(02), 212. https://doi.org/10.4236/me.2016.72023
Webber, A. G. (2001). Exchange rate volatility and cointegration in tourism demand. Journal of Travel Research, 39(4), 398-405. https://doi.org/10.1177/004728750103900406
du Preez, J., & Witt, S. F. (2003). Univariate versus multivariate time series forecasting: an application to international tourism demand. International Journal of Forecasting, 19(3), 435-451. https://doi.org/10.1016/S0169-2070(02)00057-2
Yap, G. (2012). An examination of the effects of exchange rates on Australia's inbound tourism growth: A multivariate conditional volatility approach. International Journal of Business Studies: A Publication of the Faculty of Business Administration, Edith Cowan University, 20(1), 111-132.
Wang, Y. S. (2009). The impact of crisis events and macroeconomic activity on Taiwan's international inbound tourism demand. Tourism Management, 30(1), 75-82. https://doi.org/10.1016/j.tourman.2008.04.010.

Page: 
1-19
Issue: 
Volume 2, Number 2, May-August 2018
Content Status: 
Published