Detecting of Turning Points in Business Cycles of Iranian Economy Through Autoregressive Markov Switching Model

Subject Areas : Labor and Demographic Economics

kambiz hojabr kiani ¹ , Alireza moradi ²

1 - استاد دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران
2 - دانشجوی دکتری دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران

Received: 2015-04-22 Accepted : 2015-04-22 Published : 2011-04-01

Keywords: Turning Points, Business Cycle, Autoregressive Markov Switching Model,

Abstract :

This study was to investigate, turning points in Business Cycles in the economy of Iran using seasonal date during (1981:1-2008:2). To make it practical Autoregressive Markov Switching Model by Hamilton (1989) was used. Today this approach is used in many advanced countries in order to identify and dating of cycle. Results showed that in that period in three junctures four records happened. The longest records are during [1991:2-1998:2], with the duration of 7 seasons. In addition to that results showed that in under discussion period every time a record happens in countries for about 1.74 seasons. While the appearance of every Boom in under discussion period in the economy of Iran continued 6.66 seasons.

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پیوست الف: خروجی نرم افزار MATLAB برای تخمین الگوی خودبازگشتی سوئیچینگ مارکف

تخمین حداکثر راستنمایی پارامترهای الگوی خودبازگشتی سوئیچینگ مارکف از رشد تولید فصلی کشور ایران

-------- EM algorithm converged after 99 iterations ------------ EQ(1) MSM(2)-AR(1) model of DLGDP        Estimation sample: 1367 (1) - 1387 (2) no. obs. per eq. :         82   no. parameters   :         10    linear system :          2    no. restrictions :          1   ---------- matrix of transition probabilities ------            Regime 1  Regime 2 Regime 1    0.8500    0.7480 Regime 2    0.1490    0.2510 ---------- regime properties ----------------------                nObs     Prob.  Duration Regime 1      69.7    0.8500      6.66 Regime 2      12.3    0.2510      1.33 ---------- coefficients ----------------------------                   Coef  StdError     t-val Mean (Reg.1)    2.2221    0.9308    2.3873 Mean (Reg.2)   -0.2607    0.6294   -0.4142 DIRGDP_1       -0.5455    0.4891   -1.1153 DIRGDP_2        0.8784    0.2013    4.3636   Sum log likelihood for Normal distribution - MS(2)-Ar(1)-->-216.6122 Sum log likelihood for Normal distribution - MS(2)-Ar(1)-->-216.6122 Sum log likelihood for Normal distribution - MS(2)-Ar(1)-->-216.6122 Sum log likelihood for Normal distribution - MS(2)-Ar(1)-->-216.6122 Sum log likelihood for Normal distribution - MS(2)-Ar(1)-->-216.6122 Sum log likelihood for Normal distribution - MS(2)-Ar(1)-->-216.6122 Sum log likelihood for Normal distribution - MS(2)-Ar(1)-->-216.6122 Sum log likelihood for Normal distribution - MS(2)-Ar(1)-->-216.6122 Sum log likelihood for Normal distribution - MS(2)-Ar(1)-->-216.6122 Sum log likelihood for Normal distribution - MS(2)-Ar(1)-->-216.6122 Sum log likelihood for Normal distribution - MS(2)-Ar(1)-->-216.6122 Sum log likelihood for Normal distribution - MS(2)-Ar(1)-->-216.6122   ***** MS Optimizations terminated. *****   Final log Likelihood: -216.6122           Number of parameters: 10   -----> Final Parameters <-----   Parameters in State 1:   AR param      -> -0.54549         AR param (Std)-> 0.48916 Constant      -> 2.2221           Constant (Std)-> 0.93085 Std Dev       -> 3.2532           Std Dev  (Std)-> 0.6224   Parameters in State 2:   AR param      -> 0.87836          AR param (Std)-> 0.2013 Constant      -> -0.2607          Constant (Std)-> 0.62946 Std Dev       -> 1.3126           Std Dev  (Std)-> 0.88861   ------> Transition Probabilities Matrix <-----  0.850       0.748      0.149       0.251