State-Space Models with Regime Switching

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.<p>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.</p>