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Indirect estimation of Markov switching models with endogenous switching

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  • Otranto, Edoardo
  • Calzolari, Giorgio
  • Di Iorio, Francesca

Abstract

Markov Switching models have been successfully applied to many economic problems. The most popular version of these models implies that the change in the state is driven by a Markov Chain and that the state is an exogenous discrete unobserved variable. This hypothesis seems to be too restrictive. Earlier literature has often been concerned with endogenous switching, hypothesizing a correlation structure between the observed variable and the unobserved state variable. However, in this case the classical likelihood-based methods provide biased estimators. In this paper we propose a simple “estimation by simulation” procedure, based on indirect inference. Its great advantage is in the treatment of the endogenous switching, which is about the same as for the exogenous switching case, without involving any additional difficulty. A set of Monte Carlo experiments is presented to show the interesting performances of the procedure.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 22983.

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Date of creation: 2005
Date of revision: 2005
Handle: RePEc:pra:mprapa:22983

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Keywords: Markov switching models; indirect inference; simulation estimation; Monte Carlo;

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  1. Kim, Chang-Jin & Piger, Jeremy & Startz, Richard, 2008. "Estimation of Markov regime-switching regression models with endogenous switching," Journal of Econometrics, Elsevier, Elsevier, vol. 143(2), pages 263-273, April.
  2. Ronald Gallant, A. & Tauchen, George, 1999. "The relative efficiency of method of moments estimators1," Journal of Econometrics, Elsevier, Elsevier, vol. 92(1), pages 149-172, September.
  3. Edoardo Otranto, 2005. "The multi-chain Markov switching model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(7), pages 523-537.
  4. Gallant, A. Ronald & Tauchen, George, 1996. "Which Moments to Match?," Econometric Theory, Cambridge University Press, vol. 12(04), pages 657-681, October.
  5. Di Iorio, Francesca & Calzolari, Giorgio, 2006. "Discontinuities in indirect estimation: An application to EAR models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 50(8), pages 2124-2136, April.
  6. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, Elsevier, vol. 45(1-2), pages 39-70.
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