Abstract
Since the global financial crisis erupted in September 2008, many recent economists have been worried about the health of financial institutions. Consequently, many recent researches have put great emphasis on study of total debt service ratio (TDS) as one of the early warning indicators for financial crises. Accurate TDS forecasting can have a huge impact on effective financial management as a country can monitor the signal of financial crisis from a TDS’s future trend. Therefore, the purpose of this paper is to find the modeling to forecast the growth of TDS. Autoregressive integrated moving average (ARIMA) models tends to be the most popular forecasting method with indispensable requirement of data stationarity. Meanwhile, State Space model (SSM) allows us to examine directly from original data without any data transformation for stationarity. Furthermore, it can model both structural changes or sudden jumps. The empirical result shows that the SSM expresses lower prediction errors with respect to RMSE and MAE in comparison with ARIMA.
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Navapan, K., Boonyakunakorn, P., Sriboonchitta, S. (2018). Forecasting the Growth of Total Debt Service Ratio with ARIMA and State Space Model. 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_35
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DOI: https://doi.org/10.1007/978-3-319-70942-0_35
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