This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Time Series Modeling with Duration Dependent Markov-Switching Vector Autoregressions: MCMC Inference, Software and Applications

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Matteo M. Pelagatti (University of Milan-Bicocca)

Additional information is available for the following registered author(s):

Abstract

Duration dependent Markov-switching VAR (DDMS-VAR) models are time series models with data generating process consisting in a mixture of two VAR processes, which switches according to a two-state Markov chain with transition probabilities depending on how long the process has been in a state. In the present paper I propose a MCMC-based methodology to carry out inference on the model's parameters and introduce DDMSVAR for Ox, a software written by the author for the analysis of time series by means of DDMS-VAR models. An application of the methodology to the U.S. business cycle concludes the article.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://129.3.20.41/eps/em/papers/0503/0503008.pdf
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by EconWPA in its series Econometrics with number 0503008.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 19 pages
Date of creation: 11 Mar 2005
Date of revision:
Handle: RePEc:wpa:wuwpem:0503008

Note: Type of Document - pdf; pages: 19
Contact details of provider:
Web page: http://129.3.20.41

For technical questions regarding this item, or to correct its listing, contact: (EconWPA).

Related research
Keywords: Markov-switching; Business cycle; Gibbs sampling; Duration dependence; Vector autoregression;

Find related papers by JEL classification:
C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General
C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
C5 - Mathematical and Quantitative Methods - - Econometric Modeling
C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Sichel, Daniel E, 1991. "Business Cycle Duration Dependence: A Parametric Approach," The Review of Economics and Statistics, MIT Press, vol. 73(2), pages 254-60, May. [Downloadable!] (restricted)
  2. Francis X. Diebold & Glenn D. Rudebusch & Daniel E. Sichel, 1991. "Further evidence on business cycle duration dependence," Working Papers 91-11, Federal Reserve Bank of Philadelphia.
    Other versions:
  3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March. [Downloadable!] (restricted)
  4. Diebold, Francis X & Rudebusch, Glenn D, 1990. "A Nonparametric Investigation of Duration Dependence in the American Business Cycle," Journal of Political Economy, University of Chicago Press, vol. 98(3), pages 596-616, June. [Downloadable!] (restricted)
    Other versions:
Full references

Statistics
Access and download statistics

Did you know? You may want to explore EconPapers, which displays the same data as IDEAS in a different way.

This page was last updated on 2009-11-20.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.