The extreme value forecasting in dynamics situations for reducing of economic crisis: Cases from Thailand, Malaysia, and Singapore

Abstract

This chapter was successfully proposed to clarify the complicated issue which is the dynamic prediction in the extreme events in economic cycles and computationally estimated its impacts on economic systems in ASEAN-3 countries such as Thailand, Malaysia, and Singapore by employing econometric tools, including the Markov-Switching Bayesian Vector Autoregressive model (MSBVAR), Bayesian Non-Stationary Extreme Value Analysis (NEVA), and Bayesian Dynamic Stochastic General Equilibrium approach (BDSGE). Technically, the yearly time-series variables such as Thailand’s gross domestic products, Malaysia’s gross domestic products, and Singapore’s gross domestic products were observed during 1961–2016. Empirically, the results showed the economic trends in the countries containing fluctuated movements relied on the real business cycle concept (RBC model). Additionally, these trends had unusual points called “extreme events” which should be mentioned as an economic alarming signal. Furthermore, the speedy economic adjustments estimated by BDSGE indicated that the extreme fluctuated rates of GDP in ASEAN-3 countries can be the harmful factor to face capital bubble crises, chronic unemployment, and even overpricing indexes. Accordingly, practical policies and private collaboration regarding economic alarming announcements in advance should be intensively considered.