Download PDF by James D. Hamilton, Baldev Raj: Advances in Markov-Switching Models: Applications in

By James D. Hamilton, Baldev Raj

ISBN-10: 3642511821

ISBN-13: 9783642511820

ISBN-10: 3642511848

ISBN-13: 9783642511844

This ebook is a set of state of the art papers at the houses of industrial cycles and fiscal research. the person contributions disguise new advances in Markov-switching versions with functions to enterprise cycle study and finance. The creation surveys the present tools and new result of the decade. person chapters learn positive factors of the U. S. and ecu company cycles with specific specialize in the function of financial coverage, oil shocks and co­ routine between key variables. The short-run as opposed to long-run effects of an fiscal recession also are mentioned. one other region that's featured is an intensive research of forex crises and the potential for bubbles or fads in inventory costs. A concluding bankruptcy deals necessary new effects on trying out for this sort of regime-switching behaviour. total, the ebook offers a state of the art over­ view of latest instructions in equipment and effects for estimation and inference in response to using Markov-switching time-series research. a distinct function of the booklet is that it contains a demonstration of a variety of purposes according to a typical method. it truly is anticipated that the subject of the e-book could be of specific curiosity to the macroeconomics readers in addition to econometrics execs, students and graduate scholars. we want to convey our gratitude to the authors for his or her powerful contributions and the reviewers for his or her advice and cautious realization to element of their reports.

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Extra info for Advances in Markov-Switching Models: Applications in Business Cycle Research and Finance

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The process {XI} is said to be non-sharp iff the transition probabilities to and from the two outer regimes are identical: Pml = PmM, Plm = PM"" Can oil shocks explain asymmetries in the US Business Cycle? 45 for all moll, M, where M denotes the number of regimes, and: PIM = PMI· Note that for M = 2 only the second part of the definition is operative. In a two-regime model, for example, non-sharpness implies that PI2 = P21. 2. 1(st-dl + u" utlst ~ NID(O, (}'2). 1(St) takes on one of the values PI < ...

1(St) takes on one of the values PI < ... 1M, depending on which of the M regimes the process is in. , ~111 E (0,1) for all m = I, ... , M, where ~m is the ergodic or unconditional probability of regime m. The findings of Raymond and Rich (1997) support the assumption of time-invariant transition probabilities: M pij=prob(stfl =jlst=i), LPij= I Vi,jE{l, ... ,M}. (2) j=1 Clements and Krolzig (2000) establish the following propositions concerning the restrictions on the parameter space of the MSM-AR model that correspond to the concepts of steepness and deepness (the restrictions implied by sharpness follow immediately): Proposition 1.

Formally, steepness, deepness and sharpness (SDS) can be defined as follows, letting {XI} denote the detrended series: Definition 1. Deepness. Sichel (1993). The process {XI} is said to be non-deep (non-tall) iff XI is not skewed: Analogously we can define steepness as skewness of the differences: Definition 2. Steepness. Sichel (1993). The process {XI} is said to be non-steep iffAxt is not skewed: Negative skewness of X t and Ax, indicates deep and steep contractions. uJ 3] > 0) and steep (Axi positively skewed) expansions, although all combinations are possible.

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Advances in Markov-Switching Models: Applications in Business Cycle Research and Finance by James D. Hamilton, Baldev Raj


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