Forecasting the Number of Migrant Workers in Thailand:Empirical Study and Discussion
Keywords:
Forecasting, Box-Jenkins, Migrant Workers, Thailand, Economic DevelopmentAbstract
Thailand has a large number of migrant workers which concentration in economic areas and high demand employment. The purpose of this study aims to predict the number of migrant workers who are permitted to work in Bangkok, Metropolitan, Central, North, Northeast, and South in Thailand. Time series data were theoretically used to predict using autoregressive integrated moving average (ARIMA) or Box-Jenkins forecasting method. The results showed that the ARIMA(p,d,q) was appropriate to predict the number of migrant workers. Based on the lowest value of the AIC and RMSE statistics were classified by migrant workers in Bangkok, Metropolitan, Central, North, Northeast and South with ARIMA(3,1,3), ARIMA(1,1,1), ARIMA(3,1,3), ARIMA(3,0,3), ARIMA(3,1,3) and ARIMA(3,1,3), respectively. Regarding the forecasting efficiency technique, the comparison between the actual value and the estimated value was in the range of 82.78 - 95.87% which result from the correlation coefficient. The forecasting number of migrant workers in 2018 showed that the number of migrant workers in Bangkok, North and Northeast was likely to decrease. Meanwhile, the number of migrant workers had a tendency of increasing in Metropolitan, Central, and South. However, the overall forecasting number of migrant workers in Thailand was likely to decrease by 5.68%.
References
Anantakul, A. (2017). Aging society...Challenge of Thailand. Available: http://www.royin.go.th/wp-content/uploads/2017/12.
Bijak, J., Disney, G., Findlay, A. M., Forster, J. J., Smith, P. W. & Wiśniowski, A. (2019). Assessing time series models for forecasting international migration: Lessons from the United Kingdom. Journal of Forecasting, 38(5), 470-487.
Box, G. E. P. & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. San Francisco: Holden Day.
Chang, C. L., Sriboonchitta, S. & Wiboonpongse, A. (2009). Modelling and forecasting tourism from East Asia to Thailand under temporal and spatial aggregation. Mathematics and Computers in Simulation, 79(5), 1730-1744.
Dickey, D. & Fuller, W. A. (1979). Distributions of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427-431.
Dickey, D. & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057-1072.
Dobre, I. & Alexandru, A. A. (2008). Modelling unemployment rate using Box-Jenkins procedure. Journal of Applied Quantitative Methods, 3(2), 156-166.
Dritsakis N. & Klazoglou, P. (2018). Forecasting unemployment rates in USA using Box-Jenkins methodology. International Journal of Economics and Financial Issues, 8(1), 9-20.
Granger, C. W. J. & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2(2), 111-120.
Gujarati, D. N. & Porter, D. C. (2009). Basic econometrics. (5th ed.). New York: McGraw Hill.
Lim, C., Chang, C. L. & McAleer, M. (2009). Forecasting h(m)otel guest nights in New Zealand. International Journal of Hospitality Management, 28(2), 228-235.
National Statistical Office. (2015-2017). Labor statistics: Workforce/Labor supply. Available: http://www.nso.go.th
