State-space models with regime switching : classical and Gibbs-sampling approaches with applications / Chang-Jin Kim and Charles R. Nelson.

Includes bibliographical references and index.

State-Space Models and Markov Switching in Econometrics: A Brief History -- Computer Programs and Data -- The Classical Approach -- The Maximum Likelihood Estimation Method: Practical Issues -- Maximum Likelihood Estimation and the Covariance Matrix of OML -- The Prediction Error Decomposition and the Likelihood Function -- Parameter Constraints and the Covariance Matrix of OML -- State-Space Models and the Kalman Filter -- Time-Varying-Parameter Models and the Kalman Filter -- State-Space Models and the Kalman Filter -- Application 1: A Decomposition of Real GDP and the Unemployment Rate into Stochastic Trend and Transitory Components -- Application 2: An Application of the Time-Varying-Parameter Model to Modeling Changing Conditional Variance -- Application 3: Stock and Watson's Dynamic Factor Model of the Coincident Economic Indicators -- GAUSS Programs to Accompany Chapter 3 -- Markov-Switching Models -- Introduction: Serially Uncorrelated Data and Switching -- Serially Correlated Data and Markov Switching -- Issues Related to Markov-Switching Models -- Application 1: Hamilton's Markov-Switching Model of Business Fluctuations -- Application 2: A Unit Root in a Three-State Markov-Switching Model of the Real Interest Rate -- Application 3: A Three-State Markov-Switching Variance Model of Stock Returns -- GAUSS Programs to Accompany Chapter 4 -- State-Space Models with Markov Switching -- Specification of the Model -- The Basic Filter and Estimation of the Model -- Smoothing -- An Evaluation of the Kim Filter and Approximate MLE.

"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."--Jacket.

Print version record.

English.

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