Titlebook: Markov-Switching Vector Autoregressions; Modelling, Statistic Hans-Martin Krolzig Book 1997 Springer-Verlag Berlin Heidelberg 1997 business

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https://doi.org/10.1007/978-3-642-51684-9business cycle; calculus; econometrics; emperical business cycle; forecasting; integration; modeling; regre

CODA

978-3-540-63073-9Springer-Verlag Berlin Heidelberg 1997

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In the last decade time series econometrics has changed dramatically. One increasingly prominent fion-linear mod- elling strategies. While the importance ofregime shifts, particularly in macroeconometric systems, seems to be generally accepted, there is no established theory suggesting a unique approach for specifying econometric models that embed changes in regime.

鄙視

The State-Space Representation, employ the . which has been proven useful for the study of time series with unobservable states. In order to motivate the introduction of state-space representations for MS(.)-VAR(.) models it might be helpful to sketch its use for the three main tasks of statistical inference:

CRAMP

Multi-Move Gibbs Sampling, approximate non-linear processes as piecewise linear by restricting the processes to be linear in each regime. Since the distribution of the observed variable .. is assumed normal conditional on the unobserved regime vector ξ., the MS-VAR model is well suited for Gibbs sampling techniques.

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Markov-Switching Models of the German Business Cycle,ested in the chronology of contraction and expansion epochs (cf. .. [1994]). This view is expressed in the primary descriptive definition of the ‘business cycle’ proposed by . [1946, p. 3] which is however compatible with most business cycle theories (cf. . [1995]):

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Book 1997 sists mainly of unpublished material which has been presented during the last years at conferences and in seminars. The major parts of this study were written while I was supported by the Deutsche Forschungsgemeinschajt (DFG), Berliner Graduier- tenkolleg Angewandte Mikro?konomik and Sondeiforschun