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State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications

Author

Listed:
  • Chang-Jin Kim

    () (University of Washington)

  • Charles R. Nelson

    () (University of Washington)

Abstract

Both state-space models and Markov switching models have been highly productive paths for empirical research in macroeconomics and finance. This book presents recent advances in econometric methods that make feasible the estimation of models that have both features. One approach, in the classical framework, approximates the likelihood function; the other, in the Bayesian framework, uses Gibbs-sampling to simulate posterior distributions from data. The authors present numerous applications of these approaches in detail: decomposition of time series into trend and cycle, a new index of coincident economic indicators, approaches to modeling monetary policy uncertainty, Friedman's "plucking" model of recessions, the detection of turning points in the business cycle and the question of whether booms and recessions are duration-dependent, state-space models with heteroskedastic disturbances, fads and crashes in financial markets, long-run real exchange rates, and mean reversion in asset returns.

Suggested Citation

  • Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, January.
  • Handle: RePEc:mtp:titles:0262112388
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    More about this item

    Keywords

    regime switching; gibbs-sampling; posterior distributions; likelihood function;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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