Document Type : Original Research


Faculty Member in School of Engineering, Department of Mechanics, Zabol University, Zabol, Iran


In this paper, the Iran Business Cycle characteristics were investigated via numerous univariate and multivariate Markov-switching specifications. In this case Markov switching model MSM-ARMA is proposed for determining business cycles. We examined the stochastic properties of the cyclical pattern of the quarterly Iran real GDP between 1988 (1) – 2008 (2). The empirical analysis consists of mainly three parts. First, a large number of alternative specifications were tried and few were adopted with respect to various diagnostic statistics. Then, all selected models were tested against their linear benchmarks. LR test results imply strong evidence in favor of the nonlinear regime switching behavior. In line with the main objective of research, proposed model for Iran business cycle is estimated by and result of this estimation showed that economic of Iran despite of having two periods of recession 1992(3) - 1992(4) and 1995(1)-1995(2), is out of recession with moderate growth and also experienced growth with high rate in early period of studying. Also the possibility of resistance of recession regimes with moderate and high growth is 0.3, 0.92 and 0.5 respectively. The results show the economic tend to stay in moderate growth regime.


Burns, A. F., W.C. Mitchell (1946), Measuring business cycles, New York: NBER.
Chauvet, M. (1998), An econometric characterization of business cycle dynamics with factor structure and regime switches, International Economic Review 39, (4), 969-996.
Chauvet, M. (2001), A monthly indicator of Brazilian GDP, The Brazilian Review of Econometrics Vol. 21, No. 1, (Revista de Econometrcia).
Clements, M.P., Krolzig, H.-M. (2002). Can oil shocks explain asymmetries in the US business cycle?, Empirical Economics 27, 185-204. Reprinted as pages 91-112 of: Hamilton, J.D. and B. Raj (eds) (2002) `Advances in Markov-switching models', Heidelberg: Physica.
Clements, M.P., Krolzig, H.-M. (2003), Business cycle asymmetries: characterization and testing based on Markov-switching autoregressions, Journal of Business and Economic Statistics, 21, 196 - 211
Dempster, A.P., N.M. Laird & D.B. Rubin (1977), Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, B39, 1-38.
Diebold, F. X., G.D. Rudebusch (1996), Measuring business cycles: A modern perspective, The Review of Economics and Statistics 78 (1), 67-77.
Diebold, F.X., J.H. Lee, G.C. Weinbach (1994), Regime switching with time-varying transition probabilities, in: C. Hargreaves (ed.), Nonstationary Time Series Analysis and Cointegration, Oxford: Oxford University Press, 283-302.
  Ehrmann, M., Ellison, M., Valla, N., (2003) Regime dependent impulse response functions in a vector Markov-switching model , Bank of Finland-Discussion Papers 11/2001.
Filardo, A. J. (1994), Business cycle phases and their transitional dynamics, Journal of Business and Economic Statistics 12, 299-308.
Granger, C., T. Terasvirta, H. Anderson (1993), Modeling nonlinearity over the business cycle, in: J.H. Stock and M.W. Watson (eds.) Business Cycles, Indicators and Forecasting, Chicago: University of Chicago Press for NBER, 311-325.
Hamilton, J. D. (1989), A new approach to the economic analysis of nonstationary time series and the business cycle, Econometrica 57, 357-384.
Hamilton, J. D. (1990), Analysis of time series subject to changes in regimes, Journal of Econometrics 45, 39-70.
Hamilton, J., Perez-Quiros, G. (1996), What do the leading indicators lead?, Journal of Business 69, 27-49.
Hansen, B. E. (1992), The likelihood ratio test under non-standard conditions: Testing the Markov trend model of GNP, Journal of Applied Econometrics 7, 61-82.
Hansen, B. E. (1997), Inference in TAR Models, Studies in Nonlinear Dynamics and Econometrics 2, (1), 1-14.
Kim, C.J., C. R. Nelson (1998), Business cycle turning points, A new coincident index and tests of duration dependence based on a dynamic factor model with regime switching, Review of Economics and Statistics, 80, 188-201.
Krolzig, H.-M. (1997), Markov Switching Vector Autoregressions: Modelling, Statistical Inference and Application to Business Cycle Analysis: Lecture Notes in Economics and Mathematical Systems, 454, Springer-Verlag, Berlin.
Krolzig, H.-M. (1998), Econometric modeling of Markov-switching vector autoregressions using MSVAR for Ox, Discussion Paper, Department of Economics, University of Oxford:
Krolzig, H.-M. (2000), Predicting Markov-switching vector autoregressive processes, Journal of Forecasting, forthcoming.
Krolzig, H.-M. (2001), Markov switching procedures for dating the Euro-zone business cycle, Vierteljahreshefte zur Wirtschaftsforschung, 70 (3), 339-351.
Krolzig, H.-M., Marcellino, M., Mizon, G. (2002), A Markov-switching vector equilibrium correction model of the UK labour market, Empirical Economics, 27, 233-254. Reprinted as pages 41-60 of: Hamilton, J.D.and B.Raj (eds),(2002),Advances in Markov-Switching Model,Heidelberg: Physica.
Ferrara,L.(2003), A three-regime real-time indicator for the US economy, Economics Letters,81373-378.
Pelagatti, M. (2002), Duration-dependent Markov-switching VAR models with applications to the business cycle analysis, Universita di Milano-Bicocca.