State-space models with regime switching :classical and Gibbs-sampling approaches with applications
- 作者: Kim, Chang-Jin,
- 其他作者: Nelson, Charles R.
- 出版: Cambridge, Mass. : MIT Press ©1999.
- 稽核項: 1 online resource (xii, 297 pages) :illustrations.
- 叢書名: The MIT Press Ser.
- 標題: Sampling (Statistics) , Processus de Markov. , ECONOMICS/General , Économétrie. , Méthodes de l'espace état. , EconomicsTheory. , Markov processes. , State-space methods. , Economic Theory. , Econometric models. , Econometrics. , Mathematical models. , Économie politique , Markov Chains , BUSINESS & ECONOMICS , Heteroscedasticity. , Modèles économétriques. , Economics , Économie politique Modèles mathématiques. , Economics Mathematical models. , Hétéroscédasticité. , Electronic books. , BUSINESS & ECONOMICS Economics -- Theory. , Business & Economics. , Échantillonnage (Statistique) , Modèles mathématiques.
- ISBN: 0262112388 , 9780262112383
- ISBN: 0262112388
- 試查全文@TNUA:
- 附註: Includes bibliographical references and index.
- 摘要: "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.
- 電子資源: https://dbs.tnua.edu.tw/login?url=https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=9231
- 系統號: 005283783
- 資料類型: 電子書
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- 引用網址: 複製連結
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.
來源: Google Book
來源: Google Book
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