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

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Product Description

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.

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

This book gives a step-by-step treatement of models with regime changes and time varying coefficients. If you are a student or a practitioner you will find this book very useful to start your applications. The first six chapters are very well developed, and the GAUSS codes provided by the authors let you realize how to do the job. These chapters will let you estimate a model using the classical approach. However, the next chapters that cover exactly the same topics using a bayesian approach are not that well developed. The examples and explanations are not clear, and the few examples do not help you generalize the techniques to your own models. The first six chapters, however, make this book worth 5 stars!

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