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State-Space Models with Regime Switching - Chang-Jin Kim, Charles R. Nelson

State-Space Models with Regime Switching

Classical and Gibbs-Sampling Approaches with Applications
Buch | Hardcover
312 Seiten
1999
MIT Press (Verlag)
978-0-262-11238-3 (ISBN)
CHF 109,90 inkl. MwSt
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Both state-space models and Markov switching models have been highly productive paths for empirical research in macroeconomics and finance. This book presents advances in econometric methods that make feasible the estimation of models that have both features.
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.

Chang-Jin Kim is Bryan C. Cressey Professor in the Department of Economics at the University of Washington. Charles Nelson is Ford and Louisa Van Voorhis Professor in the Department of Economics at the University of Washington.

Reihe/Serie State-Space Models with Regime Switching
Verlagsort Cambridge, Mass.
Sprache englisch
Maße 152 x 229 mm
Gewicht 612 g
Themenwelt Wirtschaft Volkswirtschaftslehre Ökonometrie
ISBN-10 0-262-11238-8 / 0262112388
ISBN-13 978-0-262-11238-3 / 9780262112383
Zustand Neuware
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