Abstract
This paper aims to computationally analyze the extreme events which can be described as crises or unusual times-series trends among the macroeconomic variables. These data are statistically estimated by employing the optimally extreme point for supporting policy makers to specify the economic expansion target and economic warning level. The Nonstationary Extreme Value Analysis (NEVA) applying Bayesian inference and Newton-optimal method are employed to complete the researchs solutions and estimate the time-series variables such as GDP, CPI, FDI, and unemployment rate collected during 1980 to 2015. The results show there are extreme values in the trend of macroeconomic factors in Thailand economic system. This extreme estimation is presented as an interval. In addition, the empirical results from the optimization approach state that the exactly extreme points can be computationally found. Ultimately, it is clear that the computationally statistical approach, especially Bayesian statistics, is inevitably important for econometric researches in the recent era.
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Appendices
Appendix A: The Bayesian extreme value estimation (Part 1)
Presentation the Bayesian extreme result regarding Thailand GDP
Presentation the Bayesian extreme result regarding CPI
Presentation the Bayesian extreme result regarding FDI
Presentation the Bayesian extreme result regarding unemployment rate
Appendix B: The Newton-optimal processing (Part 2)
Presentation the Newton-optimal point for the growth rate of GDP
Presentation the Newton-optimal point for the growth rate of CPI
Presentation the Newton-optimal point for the growth rate of FDI
Presentation the Newton-optimal point for the growth rate of unemployment
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Wannapan, S., Chaiboonsri, C., Sriboonchitta, S. (2018). Macro-Econometric Forecasting for During Periods of Economic Cycle Using Bayesian Extreme Value Optimization Algorithm. 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_51
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DOI: https://doi.org/10.1007/978-3-319-70942-0_51
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