Abstract
In this paper, we experiment with several different models with belief function to forecast Thai telephone subscribers. This approach will provide an uncertainty about predicted values and yield a predictive belief function that quantities the uncertainty about the future data. The proposed forecasting models include linear AR, Kink AR, Threshold AR, and Markov Switching AR models. Next, we compare the out-of-sample performance using RMSE and MAE. The results suggest that the out-of-sample belief function based KAR forecast is more accurate than other models. Finally, we find that the growth rate of Thai telephone subscription in 2016 will fall around 6.08%.
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References
Autchariyapanitkul, K., Chanaim, S., Sriboonchitta, S., Denoeux, T.: Predicting stock returns in the capital asset pricing model using quantile regression and belief functions. In: Third International Conference Belief Functions: Theory and Applications, September 2014, Oxford, United-Kingdom. LNAI, vol. 8764, pp. 219–226. Springer (2014)
Chan, K., Tsay, R.S.: Limiting properties of the least squares estimator of a continuous threshold autoregressive model. Biometrika 45, 413–426 (1998)
Dempster, A.P.: New methods for reasoning towards posterior distributions based on sample data. Ann. Math. Stat. 37, 355–374 (1966)
Dempster, A.P.: The Dempster-Shafer calculus for statisticians. Int. J. Approx. Reason. 48(2), 365–377 (2008)
Denouex, T.: Likelihood-based belief function: justification and some extensions to low-quality data. Int. J. Approx. Reason. 55, 1535–1547 (2014)
Hamilton, J.D.: A new approach to the economic analysis of nonstationary time series and the business cycle. Econom. J. Econom. Soci. 57, 357–384 (1989)
Hansen, B.E.: Regression kink with an unknown threshold. J. Bus. Econ. Stat. 35, 228–240 (2017)
Kanjanatarakul, O., Sriboonchitta, S., Denoeux, T.: Forecasting using belief functions: an application to marketing econometrics. Int. J. Approx. Reason. 55(5), 1113–1128 (2014)
Kanjanatarakul, O., Denoeux, T., Sriboonchitta, S.: Prediction of future observations using belief functions: a likelihood-based approach. Int. J. Approx. Reason. 72, 71–94 (2016)
Khiewngamdee, C., Yamaka, W., Sriboonchitta, S. Forecasting asian credit default swap spreads: a comparison of multi-regime models. In: Robustness in Econometrics, pp. 471–489. Springer International Publishing (2017)
Thianpaen, N., Liu, J., Sriboonchitta, S.: Time series forecast using AR-belief approach. Thai J. Math. 14, 527–541 (2016)
Tong, H.: Threshold Models in Non-linear Time Series Analysis. Lecture Notes in Statistics, vol. 21. Springer, Berlin (1983)
Shafer, G.A.: Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)
Acknowledgements
The authors are grateful to Puay Ungphakorn Centre of Excellence in Econometrics, Faculty of Economics, Chiang Mai University for the financial support.
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Chakpitak, N., Yamaka, W., Sriboonchitta, S. (2018). Comparing Linear and Nonlinear Models in Forecasting Telephone Subscriptions Using Likelihood Based Belief Functions. In: Kreinovich, V., Sriboonchitta, S., Chakpitak, N. (eds) Predictive Econometrics and Big Data. TES 2018. Studies in Computational Intelligence, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-70942-0_26
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DOI: https://doi.org/10.1007/978-3-319-70942-0_26
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