تعیین نقاط چرخش در ادوار تجاری اقتصاد ایران با استفاده از الگوی خودبازگشتی سوئیچینگ مارکف * (1387:2-1367:1)
محورهای موضوعی : اقتصاد کار و جمعیت
کامبیز هژبر کیانی
1
(استاد دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران)
علیرضا مرادی
2
(دانشجوی دکتری دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران)
کلید واژه: ادوار تجاری, الگوی خودبازگشتی سوئیچینگ مارکف,
چکیده مقاله :
مقالۀ حاضر به بررسی تعیین نقاط چرخش در ادوار تجاری در اقتصاد ایران با استفاده از داده‌های فصلی در دورۀ زمانی (1387:2-1367:1) می‌پردازد و برای عملی ساختن این مهم از رهیافت الگوی خودبازگشتی سوئیچینگ مارکف ارائه شده توسط همیلتون (1989)استفاده می‌کند. نتایج بدست آمده حکایت از آن دارد که در طی دورۀ یاد شده در سه مقطع زمانی، چهار رکود اتفاق افتاده است، طولانی ترین این رکودها در دورۀ زمانی [1372:2- 1371:2] با تداوم هفت فصل ظهور کرده است. نتایج بدست آمده بر این دلالت دارد که در دورۀ مورد بررسی هر بار وقوع رکود، بطور متوسط 74/1 فصل تداوم داشته است. این در حالی است که بروز هر دورۀ رونق در دورۀ مورد بررسی در اقتصاد ایران 66/6 فصل ادامه یافته است
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.
منابع:
ــ عباسی نژاد، حسین و شاپور محمدی.تحلیل سیکلهای تجاری ایران با استفاده از نظریۀ موجک‌ها، مجلۀ تحقیقات اقتصادی، شماره75: 1-20.
ــ هژبر کیانی کامبیز و علیرضا مرادی (1388).تخمین تولید بالقوه و شکاف تولید با استفاده از رهیافتهای فیلترینگ. مجلۀ علمی پژوهشی پژوهشنامۀ علوم اجتماعی و انسانی دانشگاه مازندران، شماره 12: 300-320.
- Albert, James H. & Chib, Siddhartha.(1993).Bays inference via gibbs sampling of autoregressive time series subject to markov mean and variance shifts. Journal of Business and Economic Statistics, 11(1): 1-15.
- Beaudry, P. and Koop, G.(1993).Do Recessions Permanently Change Output? Journal of Monetary Economics, 31(12):149-63.
- Boldin, M. D. Dating Turning Points in the Business Cycle. Journal of Business, 1994,67(1):97-130
- Burns,Arthur F. and Mitchell, Wesley E. Measuring Business Cycles. New York: National Bureau of Economic Research, 1946.
- Chauvet, M.(1998).An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching.” International Economic Review, 39(4):969-96.
- Croushore, D. & Stark, T. (2001)A real-time data set for macroeconomists. Journal of Econometrics, 105(1):111-30.
- Denton, F.T. (1971).Adjustment of monthly or quarterly series to annual totals: An approach based on quadratic minimization. Journal of the American Statistical Association, 66(333): 99–102
- Diebold, Francis X. &Rudebusch, Glenn D.(1998). Measuring business cycles: a modern perspective. The Review of Economics and Statistics, 78(1):67-77.
- Diebold, Francis X. & Rudebusch, Glenn D.(1993). The ‘plucking model’ of business fluctuations revisited. Economic Inquiry, 31(2):171-77.
- Friedman, M.(1964). Monetary studies of the national bureau, the national bureau enters its 45th year, 44th annual report. New York: National Bureau of Economic Research: 7-25.
- Ginsburg, V.A. (1973).A further note on the derivation of quarterly figures consistent with annual data. Applied Statistics, 22(3):368–374.
- Hamilton, James D.(1989).A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica. 57(2):357-84.
- Hamilton, James D. Time Series Analysis. Princeton University.
- Hansen, Bruce E.(1992). The Likelihood ratio test under nonstandard conditions: testing the markov-switching model of gnp.” Journal of Applied Econometrics, 7(Supple 0):S61-S82.
- Kim, C. Morley, J. &Piger, J.(2002). Nonlinearity and the permanent effects of recessions. Working Paper 2002-1014, Federal Reserve Bank of St. Louis,
- 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. The Review of Economics and Statistics 80 (2):188–201.
- Kim, C. J. and C. R. Nelson (1999). State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications Cambridge: MIT Press.
- Kim, C.-J. & Nelson, Charles R.(1999). Friedman’s plucking model of business fluctuations: tests and estimates of permanent and transitory components. Journal of Money, Credit, and Banking, 31(3):317-34.
- Layton, Allan P.(1996). Dating and predicting phase changes in the U.S. business cycle. International Journal of Forecasting, 12(3):417-28.
- McConnell, Margaret M.(1998). Rethinking the value of initial claims as a forecasting tool. Federal Reserve Bank of New York Current Issues in Economics and Finance, 4(11): 1-6.
- Moore, Geoffrey H. & Zarnowitz, V.(1986). The Development and role of the national bureau of economic research’s business cycle chronologies in Robert J. Gordon, ed., The American Business Cycle: Continuity and Change. Chicago: University of Chicago Press.
- Sichel, D. E(1994). Inventories and the three phases of the business cycle. Journal of Business and Economic Statistics, 12(3):269-77.
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
|