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Estimating Markov-Switching ARMA Models with Extended Algorithms of Hamilton

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This paper proposes two innovative algorithms to estimate a general class of N-state Markov-switching autoregressive moving-average (MS-ARMA) models with a sample of size T. To resolve the problem of NT possible routes induced by the presence of MA parameters, the first algorithm is built on Hamilton’s (1989) method and Gray’s (1996) idea of replacing the lagged error terms with their corresponding conditional expectations. We thus name it as the Hamilton-Gray (HG) algorithm. The second method refines the HG algorithm by recursively updating the conditional expectations of these errors and is named as the extended Hamilton-Gray (EHG) algorithm. The computational cost of both algorithms is very mild, because the implementation of these algorithms is very much similar to that of Hamilton (1989). The simulations show that the finite sample performance of the EHG algorithm is very satisfactory and is much better than that of the HG counterpart. We also apply the EHG algorithm to the issues of dating U.S. business cycles with the same real GNP data employed in Hamilton (1989). The turning points identified with the EHG algorithm resemble closely to those of the NBER’s Business Cycle Dating Committee and confirm the robustness of the findings in Hamilton (1989) about the effectiveness of Markov-switching models in dating U.S. business cycles.

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Bibliographic Info

Paper provided by Institute of Economics, Academia Sinica, Taipei, Taiwan in its series IEAS Working Paper : academic research with number 07-A009.

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Length: 29 pages
Date of creation: Oct 2007
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Handle: RePEc:sin:wpaper:07-a009

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Keywords: Markov-switching; ARMA process;

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  1. Garcia, R. & Perron, P., 1990. "An Anlysis Of The Real Interest Rate Under Regime Shifts," Papers 353, Princeton, Department of Economics - Econometric Research Program.
  2. Kim, C-J., 1991. "Dynamic Linear Models with Markov-Switching," Papers 91-8, York (Canada) - Department of Economics.
  3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  4. Billio, M. & Monfort, A. & Robert, C. P., 1999. "Bayesian estimation of switching ARMA models," Journal of Econometrics, Elsevier, vol. 93(2), pages 229-255, December.
  5. Psaradakis, Zacharias & Sola, Martin, 1998. "Finite-sample properties of the maximum likelihood estimator in autoregressive models with Markov switching," Journal of Econometrics, Elsevier, vol. 86(2), pages 369-386, June.
  6. Hamilton, James D., 1988. "Rational-expectations econometric analysis of changes in regime : An investigation of the term structure of interest rates," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 385-423.
  7. Charles Engel, 1991. "Can the Markov switching model forecast exchange rates?," Research Working Paper 91-04, Federal Reserve Bank of Kansas City.
  8. James D. Hamilton & Baldev Raj, 2002. "New directions in business cycle research and financial analysis," Empirical Economics, Springer, vol. 27(2), pages 149-162.
  9. Pagan, A.R. & Schwert, G.W., 1989. "Alternative Models For Conditional Stock Volatility," Papers 89-02, Rochester, Business - General.
  10. Bollen, Nicolas P. B. & Gray, Stephen F. & Whaley, Robert E., 2000. "Regime switching in foreign exchange rates: Evidence from currency option prices," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 239-276.
  11. Engel, Charles & Hamilton, James D, 1990. "Long Swings in the Dollar: Are They in the Data and Do Markets Know It?," American Economic Review, American Economic Association, vol. 80(4), pages 689-713, September.
  12. Gray, Stephen F., 1996. "Modeling the conditional distribution of interest rates as a regime-switching process," Journal of Financial Economics, Elsevier, vol. 42(1), pages 27-62, September.
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