Comparing the Performance of ARIMA and MS-AR Models to Forecast Business Cycles in Iran

Subject Areas : Labor and Demographic Economics

Mehdi  Fazel ¹ (کارشناس ارشد توسعه اقتصادی و برنامه ریزی)
Akbar  Tavakoli ² (دانشیار اقتصاد دانشگاه صنعتی اصفهان)
Mostafa  Rajabi ³ (استادیار دانشگاه آزاد اسلامی واحد خمینی شهر)

Received: 2015-04-12 Accepted : 2015-04-12 Published : 2013-04-01

Keywords: E32, C24, E37,

Abstract :

It is clear that business cycles are inevitable in economy. On the other hand, the economists are always looking for how to form business cycles and so under the effect of economic policies, since the economic situation is depended to these policies. Therefore, the access to more precise business cycles forecasting methods would direct and manage the economic situation and policies powerfully. Hence, the main objective of this study is to construct a new model based on Markov-Switching Autoregressive (MS-AR) model to forecast the business cycles in Iran. In addition, the model constructed is compared to ARIMA to represent its power. GDP data seasonally covers the period 1989: I – 2009: IV collected from Central Bank of Iran. MS-AR and ARIMA models are applied to forecast the behavior of business cycles. By using MAPE, RMSE and Theil criteria (TIC), the results indicate that MS-AR model will work better than ARIMA to forecast GDP business cycles.

References:

منابع     

-  ‌ ختایی، محمود، دانش جعفری، داوود (1380). نماگر دوران‌های اقتصادی، فصل نامه پژوهش‌نامه بازرگانی، 5 (18): 28-1.

-  ‌ چینلار، ارهان (1380). آشنایی با فرآیندهای تصادفی. موسسه انتشارات علمی دانشگاه صنعتی شریف، تهران.

-  ‌ صیادزاده، علی، جمال دیکاله، آلن (1387). بررسی ویژگی‌های ادوار تجاری در ایران در دوره 1385-1338. فصل نامه پژوهش ها و سیاست های اقتصادی، 16 (46): 82-63.

-  ‌ محمدی، تیمور، صفرزاده، اسماعیل، موسوی، میرحسین (1388). شناسایی نقاط چرخشی دوران‌های اقتصادی ایران در یک زمان واقعی. فصل نامه پژوهش‌های اقتصادی، 9 (3): 89-65.

-  ‌ هادیان، ابراهیم، هاشم پور، محمدرضا (1382). شناسایی چرخه‌های تجاری در اقتصاد ایران. فصل نامه پژوهش‌های اقتصادی ایران، 2(15): 120-93.

-  ‌ هوشمند، محمد، فلاحی، محمدعلی، توکلی قوچانی، سپیده (1387)، تحلیل ادوار تجاری ایران با استفاده از فیلتر هادریک- پرسکات. مجله دانش و توسعه، 15 (22): 55-29.

 

 

-   Abel, A. B., & Bernanke, B. S., & Smith, Gregory, W. (2003). Macroeconomics (Addison-Wesley series in economics). Addison-Wesley publishing, Canada.

-   Beveridge, S., & Charles.R N. (1981). A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the business cycle. Journal of Monetary Economics, (7): 151-174.

-   Buss, G. (2010). Forecasts with single-equation Markov Switching Model, an application to the gross domestic product of Latvia. Journal of Applied Economic Sciences, (5):48-58. 

-   Campbell, J. Y., & Mankiw, N G. (1987). Permanent and transitory components in macroeconomic fluctuations. American Economic Review Papers and Proceedings, (77): 111-117.

-   Caraiani, P. (2010). Modeling business cycles in the Romanian Economy using the Markov Switching approach. Romanian Journal of Economic Forecasting, (1):130-136.

-   Céspedes, B. J. V., & Chauvet, M., & Lima, Elcyon C. R.(2006). Forecasting Brazilian output and its turning points in the presence of breaks: A comparison of linear and nonlinear models, Estud. Econ. 36)1(: 5-46.

-   Clements, M. P., & Krolzig, H. M. (1997). A comparison of the forecast performance of Markov-Switching and threshold autoregressive models of US GNP. The Econometrics Journal, 1(1): 47–75.

-   Hamilton, J. D. (1989). A new approach to the economic analysis of non-stationary time series and the business cycle. Econometrica 57(2):357–384.

-   Hamilton, J. D. (1994). Time series analysis, Princeton university press, Princeton, NJ.

-   Kontolemis, Z. G. (1999). Analysis of the U.S. business cycle with a Vector-Markov-Switching Model. Journal of  Forecastin, 20(1):47-61.

-   Nelson, Ch. R., & Charles I .P. (1982). Trends and random walks in macroeconomic time series: Some evidence and implications. Journal of Monetary Economics, 10(2): 139–162.

-   Olofsson, P. (2005). Probability, statistics, and stochastic processes. John Wiley & Sons Inc. Hoboken, New Jersey

-   Sulliran, A., & Sheffrin, S. M. (2006). Economics: Principles Inaction, Pearson Prentice Hall, California.

-   Tijms, H.C. (2003). A first course in stochastic models, John Wiley & Sons Ltd. England.