The Extreme Value Forecasting in Dynamics Situations for Reducing of Economic Crisis: Cases from Thailand, Malaysia, and Singapore

  • Chukiat Chaiboonsri
  • Satawat Wannapan
Part of the Contributions to Economics book series (CE)


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.


Gross domestic product ASEAN-3 Bayesian inference RBC model MSBVAR model NEVA analysis BDSGE model 


  1. Adenomon, M. O., Michael, V. A., & Evans, O. P. (2015). Short term forecasting performance of classical VAR and Sims-Zha Bayesian VAR models for time series with collinear variables and correlated error terms. Open Journal of Statistic, 5, 742–753.CrossRefGoogle Scholar
  2. Adolfson, M., Laseén, S., Lindé, J., & Villani, M. (2007). Bayesian estimation of an open economy DSGE model with incomplete pass-through. Journal of International Economics, 72(2), 481–511.CrossRefGoogle Scholar
  3. Alp, H., & Elekdag, S. (2012). Shock therapy! What role for Thai monetary policy?. IMF Working Paper 12/269. Asia and Pacific Department, International Monetary Fund.CrossRefGoogle Scholar
  4. Asian Development Bank. (2016). Key indicators for Asia and the Pacific 2015. Available at
  5. Association of Southeast Asian Nation. (2016). Asean statistical yearbook 2014. Available at
  6. Bank of Thailand. (2016). Thailand’s macro economic indicators 1. Available at
  7. Bauwens, L., Lubrano, M., & Richard, J. F. (2000). Bayesian inference in dynamic econometric models (1st ed.). Oxford: Oxford Scholarship Press.CrossRefGoogle Scholar
  8. Behrens, C. N., Lopes, H. F., & Gamerman, D. (2004). Bayesian analysis of extreme events with threshold estimation. Statistical Modelling, 4, 227–244.CrossRefGoogle Scholar
  9. Berg, A. (1999). The Asia crisis: Causes, policy responses, and outcomes. IMF Working Paper No. 138. Asia and Pacific Department, International Monetary Fund.CrossRefGoogle Scholar
  10. Boudebbous, T. (2015). Stock market bear regime and recession: Are they synchronized? International Journal of Economics and Finance, 7(2), 261–272.CrossRefGoogle Scholar
  11. Brandt, P. T. (2009). Empirical, regime-specific models of international, inter-group conflict, and politics. Paper presented at the annual meeting of the Midwest Political Science Association 67th Annual National Conference. The Palmer House Hilton, Chicago, IL (Online). November 29, 2014, from
  12. Brandt, P. T., Freeman, J. R., & Schrodt, P. A. (2011). Real time, time series forecasting of inter- and intra-state political conflict. Conflict Management and Peace Science, 28(1), 41–64.CrossRefGoogle Scholar
  13. Burns, A. F. (1979). The anguish of central banking offsite link. The 1979 Per Jacobsson Lecture, Belgrade, Yugoslavia, September 30, 1979.Google Scholar
  14. Chaiboonsri, C. (2015). Business cycle theory (1st edn). Faculty of Economics, Chiang Mai University. isbn:978-616-382-383-0.Google Scholar
  15. Chaiboonsri, C., Chaitip, P., & Chokethaworn, K. (2016). The multiplex of forecasting in extreme data: Evidences from ASEAN stock exchanges. Presented at the SIBR 2016 Conference on Interdisciplinary Business and Economics Research, 2nd–3rd June 2016, Bangkok.Google Scholar
  16. Cheng, L., AghaKouchak, A., Gilleland, E., & Katz, R. (2014). Non-stationary extreme value analysisin a changing climate. Climatic Change, 127(2), 353–369. Scholar
  17. Chow, H. K., & McNelis, P. D. (2010). Need Singapore fear floating? A DSGE-VAR approach. Working Paper No. 29. Research Collection School of Economics. Available at
  18. Chow-Tan, H. K., Lim, G. C., & McNelis, P. D. (2014). Monetary regime choice in Singapore: Would a Taylor rule outperform exchange-rate management? Journal of Asian Economics, 30, 63–81.CrossRefGoogle Scholar
  19. Collard, F., & Juillard, M. (2001). Accuracy of stochastic perturbation methods: The case of asset pricing models. Journal of Economic Dynamics and Control, 25(6–7), 979–999.CrossRefGoogle Scholar
  20. Collier, A. J. 2010. Extreme value analysis of non-stationary processes – A study of extreme rainfall under changing climate. Doctor of Philosophy, School of Civil Engineering and Geosciences, University of Newcastle.Google Scholar
  21. Duca, G. (2007). The relationship between the stock market and the economy: Experience from international financial markets. Bank of Valleta Review, 36, 1–12.Google Scholar
  22. Fernández-Villaverde, J. (2010). The econometrics of DSGE models. SERIEs, 1, 3–49. Scholar
  23. Geweke, J. (1989). Bayesian inference in econometric models using Monte Carlo integration. Econometrica, 57(6), 1317–1339.CrossRefGoogle Scholar
  24. Geweke, J. 1998. Using simulation methods for Bayesian econometric models: Inference, development, and communication. Research Department Staff Report 249, Federal Reserve Bank of Minneapolis.Google Scholar
  25. Geweke, J., & Amisano, G. (2014). Analysis of variance for Bayesian inference. Econometric Reviews, 33, 270–288.CrossRefGoogle Scholar
  26. Griffoli, T. M. (2013). An introduction to the solution and estimation of DSGE models. Boston, MA: The Free Software Foundation.Google Scholar
  27. Hamilton, J. (1989). A new approach to the economic analysis of nonstationary time series and business cycle. Econometrica, 57(2), 357–384.CrossRefGoogle Scholar
  28. Hounkpe, J., Diekkrüger, B., Badou, D. F., & Afouda, A. A. (2015). Non-stationary flood frequency analysis in the Ouémé River Basin, Benin Republic. Hydrology, 2, 210–229.CrossRefGoogle Scholar
  29. Hundecha, Y., St-Hilaire, A., Ouarda, T. B. M. J., & El Adlouni, S. (2008). A nonstationary extreme value analysis for the assessment of changes in extreme annual wind speed over the Gulf of St. Lawrence, Canada. Journal of Applied Meteorology and Climatatology, 47, 2745–2757.CrossRefGoogle Scholar
  30. Jonung, L., Kiander, J., & Vartia, P. (2008). The great financial crisis in Finland and Sweden: The dynamics of boom, bust and recovery, 1985–2000. Economic Papers 350. Directorate-General for Economic and Financial Affairs, European Commission.Google Scholar
  31. Kliem, M., & Uhlig, H. (2013). Bayesian estimation of a DSGE model with asset prices. Working Paper No. 37. Deutsche Bundesbank, Frankfurt, Germany.Google Scholar
  32. Kline, B., & Tamer, E. (2016). Bayesian inference in a class of partially identified models. Quantitative Economics, 7, 329–366.CrossRefGoogle Scholar
  33. Koop, G., Leon-Gonzalez, R., & Strachan, R. (2008). Bayesian inference in a cointegrating panel data model. In S. Chib, W. Griffiths, G. Koop, & D. Terrell (Eds.), Bayesian econometrics (Advances in econometrics) (Vol. 23, pp. 433–469). Bingley: Emerald Group.CrossRefGoogle Scholar
  34. Mallick, S., & Sousa, R. M. (2009) Monetary policy and economic activity in the BRICS. Working Paper No. 27. NIPE, The Portuguese Foundation Science and Technology.Google Scholar
  35. Mankiw, N. G. (1989). Real business cycles: A new Keynesian perspective. Journal of Economic Perspectives, 3(3), 79–90.CrossRefGoogle Scholar
  36. Moreira, R. R., Chaiboonsri, C., & Chaitip, P. (2013). Relationships between effective and expected interest rates as a transmission mechanism for monetary policy: Evidence on the Brazilian economy using MS-models and a Bayesian VAR. Procedia Economics and Finance, 5, 562–570.CrossRefGoogle Scholar
  37. Pickands, J. (1975). Statistical inference using extreme order statistics. Annals of Statistics, 3, 110–131.Google Scholar
  38. Sánchez, M. (2011). Financial crises: Prevention, correction, and monetary policy. Cato Journal, 31(3), 521–534.Google Scholar
  39. Shaari, M. H. (2008). Analyzing bank Negara Malaysia’s behavior in formulation monetary policy: An empirical approach. A thesis for the degree of Doctor of Philosophy. College of Business and Economics. The Australian National University.Google Scholar
  40. Sims, C. A., & Zha, T. A. (1998). Bayesian methods for dynamic multivariate models. International Economic Review, 39(4), 949–968.CrossRefGoogle Scholar
  41. Spirtes, P. (2005). Graphical models, causal inference, and econometric models. Journal of Economic Methodology, 12(1), 1–33.CrossRefGoogle Scholar
  42. Stadler, G. W. (1994). Real business cycles. Journal of Economics Literature, 32, 1750–1783.Google Scholar
  43. Takaishi, T. (2010). Bayesian inference with an adaptive proposal density for GARCH models. Journal of Physics: Conference Series, 221.Google Scholar
  44. Tanboon, S. (2008). The bank of Thailand structural model for policy analysis. Discussion Paper. Bank of Thailand.Google Scholar
  45. Verdick, S., & Islam, I. (2010). The great recession of 2008–2009: Causes, consequences and policy responses. Discussion Paper No. 4934. The Institute for the Study of Labor, Bonn, Germany.Google Scholar
  46. Walsh, C. E. (2010). Monetary theory and policy (3rd ed.). Cambridge, MA: The MIT Press.Google Scholar
  47. Zare, R., Azali, M., Habibullah, M. S., & Azman-Saini, W. N. W. (2013). Monetary policy effectiveness and stock market cycles in ASEAN-5. PROSIDING PERKEM VIII, 1, pp. 480–492.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Chukiat Chaiboonsri
    • 1
  • Satawat Wannapan
    • 1
  1. 1.Faculty of EconomicsChiang Mai UniversityChiang MaiThailand

Personalised recommendations