[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/p/hhs/lunewp/2004_004.html
   My bibliography  Save this paper

Reconnecting the Markov Switching Model with Economic Fundamentals

Author

Listed:
  • Erlandsson, Ulf

    (Department of Economics, Lund University)

Abstract
This paper seeks to investigate and remedy the apparent inability of Markov regime switching models to predict future states in the medium to long term. We show that projected time varying transition probability series in the model may be biased towards predicting regime switches with high probability in the short run, and as a consequence it is hard or impossible to obtain longer run inference. We propose a penalized maximum likelihood estimator where non-smoothness in the transition series has negative influence on the likelihood function, which is shown to remedy the short run bias. In an empirical investigation of U.S. real GDP, the penalized model works better in terms of forecasting future recessions as defined by the NBER business cycle dating.

Suggested Citation

  • Erlandsson, Ulf, 2004. "Reconnecting the Markov Switching Model with Economic Fundamentals," Working Papers 2004:4, Lund University, Department of Economics, revised 04 Nov 2004.
  • Handle: RePEc:hhs:lunewp:2004_004
    as

    Download full text from publisher

    File URL: https://lucris.lub.lu.se/ws/portalfiles/portal/195338706/WP04_4
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Patrick J. Coe, 2002. "Power issues when testing the Markov switching model with the sup likelihood ratio test using U.S. output," Empirical Economics, Springer, vol. 27(2), pages 395-401.
    2. Gray, Stephen F., 1996. "Modeling the conditional distribution of interest rates as a regime-switching process," Journal of Financial Economics, Elsevier, vol. 42(1), pages 27-62, September.
    3. Francis X. Diebold & Joon-Haeng Lee & Gretchen C. Weinbach, 1993. "Regime switching with time-varying transition probabilities," Working Papers 93-12, Federal Reserve Bank of Philadelphia.
    4. Tobias Rydén & Timo Teräsvirta & Stefan Åsbrink, 1998. "Stylized facts of daily return series and the hidden Markov model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(3), pages 217-244.
    5. Cheung, Yin-Wong & Erlandsson, Ulf G., 2005. "Exchange Rates and Markov Switching Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 314-320, July.
    6. 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.
    7. Filardo, Andrew J. & Gordon, Stephen F., 1998. "Business cycle durations," Journal of Econometrics, Elsevier, vol. 85(1), pages 99-123, July.
    8. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
    9. Mr. Abdul d Abiad, 2003. "Early Warning Systems: A Survey and a Regime-Switching Approach," IMF Working Papers 2003/032, International Monetary Fund.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arias, Guillaume & Erlandsson, Ulf, 2004. "Regime switching as an alternative early warning system of currency crises - an application to South-East Asia," Working Papers 2004:11, Lund University, Department of Economics.

    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. Erlandsson, Ulf, 2005. "Transition Variables in the Markov-switching Model: Some Small Sample Properties," Working Papers 2005:25, Lund University, Department of Economics.
    2. Andrew J. Filardo, 1998. "Choosing information variables for transition probabilities in a time-varying transition probability Markov switching model," Research Working Paper 98-09, Federal Reserve Bank of Kansas City.
    3. Arias, Guillaume & Erlandsson, Ulf, 2004. "Regime switching as an alternative early warning system of currency crises - an application to South-East Asia," Working Papers 2004:11, Lund University, Department of Economics.
    4. Chew Lian Chua & Sandy Suardi, 2005. "Is There a Unit Root in East-Asian Short-Term Interest Rates?," Melbourne Institute Working Paper Series wp2005n14, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    5. Alexander, Carol & Kaeck, Andreas, 2008. "Regime dependent determinants of credit default swap spreads," Journal of Banking & Finance, Elsevier, vol. 32(6), pages 1008-1021, June.
    6. Chung-Ming Kuan, 2013. "Markov switching model (in Russian)," Quantile, Quantile, issue 11, pages 13-40, December.
    7. Roland G. Shami & Catherine S. Forbes, 2002. "Non-linear Modelling of the Australian Business Cycle using a Leading Indicator," Monash Econometrics and Business Statistics Working Papers 5/02, Monash University, Department of Econometrics and Business Statistics.
    8. Laurent Calvet & Adlai Fisher, 2003. "Regime-Switching and the Estimation of Multifractal Processes," NBER Working Papers 9839, National Bureau of Economic Research, Inc.
    9. Ang, Andrew & Bekaert, Geert, 2002. "Regime Switches in Interest Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 163-182, April.
    10. Pelletier, Denis, 2006. "Regime switching for dynamic correlations," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 445-473.
    11. Carol Alexander & Andreas Kaeck, 2006. "Regimes in CDS Spreads: A Markov Switching Model of iTraxx Europe Indices," ICMA Centre Discussion Papers in Finance icma-dp2006-08, Henley Business School, University of Reading.
    12. Yuan, Chunming, 2011. "Forecasting exchange rates: The multi-state Markov-switching model with smoothing," International Review of Economics & Finance, Elsevier, vol. 20(2), pages 342-362, April.
    13. John M. Maheu & Thomas H. McCurdy, 2002. "Nonlinear Features of Realized FX Volatility," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 668-681, November.
    14. Mount, Timothy D. & Ning, Yumei & Cai, Xiaobin, 2006. "Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters," Energy Economics, Elsevier, vol. 28(1), pages 62-80, January.
    15. Cheung, Yin-Wong & Erlandsson, Ulf G., 2005. "Exchange Rates and Markov Switching Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 314-320, July.
    16. Robinson Kruse & Michael Frömmel & Lukas Menkhoff & Philipp Sibbertsen, 2012. "What do we know about real exchange rate nonlinearities?," Empirical Economics, Springer, vol. 43(2), pages 457-474, October.
    17. Andrea Carriero & Massimiliano Marcellino, 2011. "Sectoral Survey‐based Confidence Indicators for Europe," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(2), pages 175-206, April.
    18. Joanna Janczura & Rafał Weron, 2012. "Efficient estimation of Markov regime-switching models: An application to electricity spot prices," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(3), pages 385-407, July.
    19. Andrew Stuart Duncan & Guangling“dave” Liu, 2009. "Modelling South African Currency Crises As Structural Changes In The Volatility Of The Rand," South African Journal of Economics, Economic Society of South Africa, vol. 77(3), pages 363-379, September.
    20. Lhuissier, Stéphane, 2022. "Financial conditions and macroeconomic downside risks in the euro area," European Economic Review, Elsevier, vol. 143(C).

    More about this item

    Keywords

    regime switching; transition probability; forecasting;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hhs:lunewp:2004_004. 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: Iker Arregui Alegria (email available below). General contact details of provider: https://edirc.repec.org/data/delunse.html .

    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.