IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v21y2014i6p397-401.html
   My bibliography  Save this article

Identify regimes in post-war US GDP growth

Author

Listed:
  • Yu Jiang
  • Xianming Fang

Abstract

This article attempts to simultaneously investigate different regimes in both mean and volatility of post-war US GDP growth using a four-regime Bayesian Markov switching model. Bayesian approach suffers from the label switching problem that leads to the failure of identifying regimes. We introduce two methods to deal with the label switching problem in posterior simulations of parameters. The four regimes identified by either of the two methods capture different characteristics in mean and volatility of US GDP growth.

Suggested Citation

  • Yu Jiang & Xianming Fang, 2014. "Identify regimes in post-war US GDP growth," Applied Economics Letters, Taylor & Francis Journals, vol. 21(6), pages 397-401, April.
    • Handle: RePEc:taf:apeclt:v:21:y:2014:i:6:p:397-401
    DOI: 10.1080/13504851.2013.861582
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13504851.2013.861582
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13504851.2013.861582?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    2. 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 357-384, March.
    3. Geweke, John & Jiang, Yu, 2011. "Inference and prediction in a multiple-structural-break model," Journal of Econometrics, Elsevier, vol. 163(2), pages 172-185, August.
    4. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
    5. Geweke, John, 2007. "Interpretation and inference in mixture models: Simple MCMC works," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3529-3550, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, Yu, 2020. "Identification of business cycles and the Great Moderation in the post-war U.S. economy," Economics Letters, Elsevier, vol. 190(C).
    2. Shu-Ping Shi & Yong Song, 2012. "Identifying Speculative Bubbles with an Infinite Hidden Markov Model," Working Paper series 26_12, Rimini Centre for Economic Analysis.
    3. Paap, Richard & Segers, Rene & van Dijk, Dick, 2009. "Do Leading Indicators Lead Peaks More Than Troughs?," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 528-543.
    4. Luc Bauwens & Jean-François Carpantier & Arnaud Dufays, 2017. "Autoregressive Moving Average Infinite Hidden Markov-Switching Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(2), pages 162-182, April.
    5. Qian, Hang, 2011. "Bayesian Portfolio Selection in a Markov Switching Gaussian Mixture Model," MPRA Paper 35561, University Library of Munich, Germany.
    6. Fisher, Mark & Jensen, Mark J., 2019. "Bayesian inference and prediction of a multiple-change-point panel model with nonparametric priors," Journal of Econometrics, Elsevier, vol. 210(1), pages 187-202.
    7. Yong Song & Tomasz Wo'zniak, 2020. "Markov Switching," Papers 2002.03598, arXiv.org.
    8. Balcombe, Kelvin & Fraser, Iain, 2017. "Do bubbles have an explosive signature in markov switching models?," Economic Modelling, Elsevier, vol. 66(C), pages 81-100.
    9. Paroli, Roberta & Spezia, Luigi, 2008. "Bayesian inference in non-homogeneous Markov mixtures of periodic autoregressions with state-dependent exogenous variables," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2311-2330, January.
    10. Chuku Chuku & Paul Middleditch, 2020. "Characterizing Monetary and Fiscal Policy Rules and Interactions when Commodity Prices Matter," Manchester School, University of Manchester, vol. 88(3), pages 373-404, June.
    11. Hou, Chenghan, 2017. "Infinite hidden markov switching VARs with application to macroeconomic forecast," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1025-1043.
    12. Jia Liu & John M. Maheu & Yong Song, 2024. "Identification and forecasting of bull and bear markets using multivariate returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 723-745, August.
    13. Chan, Joshua C.C. & Santi, Caterina, 2021. "Speculative bubbles in present-value models: A Bayesian Markov-switching state space approach," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    14. Masaru Chiba, 2023. "Robust and efficient specification tests in Markov-switching autoregressive models," Statistical Inference for Stochastic Processes, Springer, vol. 26(1), pages 99-137, April.
    15. Sylvia Frühwirth‐Schnatter & Sylvia Kaufmann, 2006. "How do changes in monetary policy affect bank lending? An analysis of Austrian bank data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 275-305, April.
    16. Brian Hartley, 2020. "Corridor stability of the Kaleckian growth model: a Markov-switching approach," Working Papers 2013, New School for Social Research, Department of Economics, revised Nov 2020.
    17. Francesco Bianchi & Leonardo Melosi, 2017. "Escaping the Great Recession," American Economic Review, American Economic Association, vol. 107(4), pages 1030-1058, April.
    18. Sylvia Kaufmann, 2003. "The business cycle of European countries Bayesian clustering of country - individual IP growth series," Working Papers 83, Oesterreichische Nationalbank (Austrian Central Bank).
    19. Azar, Jose, 2009. "Electric Cars and Oil Prices," MPRA Paper 15538, University Library of Munich, Germany.
    20. Łukasz Kwiatkowski, 2011. "Bayesian Analysis of a Regime Switching In-Mean Effect for the Polish Stock Market," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 3(4), pages 187-219, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:apeclt:v:21:y:2014:i:6:p:397-401. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.