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.
Using a Bayesian model comparison strategy, we search for a volatility reduction within the postwar sample for the growth rates of U.S. aggregate and disaggregate real GDP. We find that the growth rate of aggregate real GDP has been less volatile since the early 1980s, and that this volatility reduction is concentrated in the cyclical component of real GDP. The growth rates of many of the broad production sectors of real GDP display similar reductions in volatility, suggesting the aggregate volatility reduction does not have a narrow source. We also find a large volatility reduction in measures of final sales in the goods sector. We contrast this evidence to an existing literature documenting an aggregate volatility reduction that is shared by only one narrow sub-component, the production of durable goods, and is not present in final sales. We also document structural breaks in the persistence and conditional volatility of inflation that occurred over a similar time frame as the volatility reduction in real GDP.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.