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

By James D. Hamilton, Baldev Raj

This ebook is a set of state of the art papers at the homes of commercial cycles and fiscal research. the person contributions conceal new advances in Markov-switching versions with functions to enterprise cycle learn and finance. The creation surveys the prevailing equipment and new result of the decade. person chapters learn beneficial properties of the U. S. and eu company cycles with specific concentrate on the function of economic coverage, oil shocks and co­ activities between key variables. The short-run as opposed to long-run results of an financial recession also are mentioned. one other quarter that's featured is an in depth research of forex crises and the potential for bubbles or fads in inventory costs. A concluding bankruptcy bargains invaluable new effects on checking out for this sort of regime-switching behaviour. total, the booklet offers a state of the art over­ view of recent instructions in equipment and effects for estimation and inference according to using Markov-switching time-series research. a unique function of the e-book is that it contains an indication of a variety of purposes in response to a typical method. it really is anticipated that the subject matter of the e-book can be of specific curiosity to the macroeconomics readers in addition to econometrics pros, students and graduate scholars. we want to show our gratitude to the authors for his or her powerful contributions and the reviewers for his or her counsel and cautious awareness 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 calculation of the Lee, Ni and Ratti variable is detailed by Hamilton. The variable is based on the real oil price variable, where the deflator is the implicit GOP deflator. The real quarterly change is divided by the conditional standard deviation derived from a GARCH model. The GOP series is real, chain-weighted in 1996 dollars, and seasonally adjusted. The employment series used in the bivariate model is calculated from seasonally adjusted, monthly figures of total nonfarm payroll employment.

Table 1 offers one measure of the match between the MS models' inferred probabilities of expansions and contractions and the NBER chronology. The 'Univariate, 2-state' model in the table is the MSM(M)-AR(p) defined in (1) with two regimes (M = 2) and p = 2. The 'Univariate, 3-state' model is the MSIH(3)-AR(4) model of equation (5). 3. 'Synchronization' reports the frequency with which the NBER-dated cycle and the Markov chain are in the same state M. P. -M. Krolzig 50 Table 1. 0429 The models are: the MSM(2)-AR(0) model, the MSJH(3)-AR(4) model, and a bivariate MSVECM model of output and employment growth.

M. Krolzig 48 the order in which 'insignificant' variables (or groups of variables) are deleted. We use David F. Hendry and Hans-Martin Krolzig's PcGets to automate the simplification procedure. By searching all the possible paths, and then comparing all the candidate models so obtained, the problem of path-dependence is solved: see Krolzig and Hendry (2001) for details of the algorithms. The Pc Gets program can be used in several modes. Initially, we employed the 'outlier correction' facility, which automatically adds impulse dummies to the general model to correct for outliers, before simplifying.

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