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

Chhorn, Theara and Chaiboonsri, Chukiat (2017): Modelling and Forecasting Tourist Arrivals to Cambodia: An Application of ARIMA-GARCH Approach. Published in: Journal of Management, Economics, and Industrial Organization , Vol. 2, No. 2 (2018): pp. 1-19.

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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.

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