IDEAS home Printed from https://ideas.repec.org/p/ctl/louvir/2003008.html
   My bibliography  Save this paper

Identifying and Forecasting the Turns of the Japanese Business Cycle

Author

Listed:
  • Konstantin A., Kholodilin

    (UNIVERSITE CATHOLIQUE DE LOUVAIN, Institut de Recherches Economiques et Sociales (IRES))

Abstract

In this paper we identify and try to predict the turning points of the Japanese business cycle. As a measure of the business cycle we use a composite economic indicator (CEI). This indicator is endowed with nonlinear dynamics to capture the asymmetries between different cyclical phases. Two types of nonlinear dynamics are considered : Markov switching and smooth transition autoregression (STAR). The performance of these models in terms of forecasting the business cycle turns is compared. Both types of models produce statistically equivalent in-sample forecasting results, whilst the CEI with exponential STAR tends to outperform the CEI with Markov-switching and logistic STAR in the out-of-sample prediction.

Suggested Citation

  • Konstantin A., Kholodilin, 2003. "Identifying and Forecasting the Turns of the Japanese Business Cycle," LIDAM Discussion Papers IRES 2003008, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    • Handle: RePEc:ctl:louvir:2003008
    as

    Download full text from publisher

    File URL: http://sites.uclouvain.be/econ/DP/IRES/2003-8.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    2. K. S. Chan & H. Tong, 1986. "On Estimating Thresholds In Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(3), pages 179-190, May.
    3. Konstantin A. Kholodilin, 2002. "Two Alternative Approaches to Modelling the Nonlinear Dynamics of the Composite Economic Indicator," Economics Bulletin, AccessEcon, vol. 3(26), pages 1-18.
    4. Sylvia Kaufmann, 2000. "Measuring business cycles with a dynamic Markov switching factor model: an assessment using Bayesian simulation methods," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 39-65.
    5. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    6. Layton, Allan P. & Katsuura, Masaki, 2001. "Comparison of regime switching, probit and logit models in dating and forecasting US business cycles," International Journal of Forecasting, Elsevier, vol. 17(3), pages 403-417.
    7. Diebold, Francis X & Rudebusch, Glenn D, 1989. "Scoring the Leading Indicators," The Journal of Business, University of Chicago Press, vol. 62(3), pages 369-391, July.
    8. Álvaro Escribano & Oscar Jordá, 2001. "Testing nonlinearity: Decision rules for selecting between logistic and exponential STAR models," Spanish Economic Review, Springer;Spanish Economic Association, vol. 3(3), pages 193-209.
    9. Gerhard Bry & Charlotte Boschan, 1971. "Foreword to "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs"," NBER Chapters, in: Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, pages -1, National Bureau of Economic Research, Inc.
    10. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-996, November.
    11. repec:ebl:ecbull:v:3:y:2002:i:26:p:1-18 is not listed on IDEAS
    12. repec:bla:jecsur:v:13:y:1999:i:5:p:505-28 is not listed on IDEAS
    13. repec:ebl:ecbull:v:3:y:2002:i:25:p:1-17 is not listed on IDEAS
    14. Shin-ichi Fukuda & Takashi Onodera, 2001. "A New Composite Index of Coincident Economic Indicators in Japan: How can we improve the forecast performance? ," CIRJE F-Series CIRJE-F-101, CIRJE, Faculty of Economics, University of Tokyo.
    15. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    16. Simon Potter, 1999. "Nonlinear Time Series Modelling: An Introduction," Journal of Economic Surveys, Wiley Blackwell, vol. 13(5), pages 505-528, December.
    17. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1.
    18. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    19. Chauvet, Marcelle, 2001. "A Monthly Indicator of Brazilian GDP," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 21(1), May.
    20. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, 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. repec:ebl:ecbull:v:3:y:2002:i:26:p:1-18 is not listed on IDEAS
    2. Vincent, BODART & Konstantin, KHOLODILIN & Fati, SHADMAN-MEHTA, 2005. "Identifying and Forecasting the Turning Points of the Belgian Business Cycle with Regime-Switching and Logit Models," Discussion Papers (ECON - Département des Sciences Economiques) 2005006, Université catholique de Louvain, Département des Sciences Economiques.
    3. Vincent, BODART & Konstantin A., KHOLODILIN & Fati, SHADMAN-MEHTA, 2003. "Dating and Forecasting the Belgian Business Cycle," LIDAM Discussion Papers IRES 2003018, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    4. Fabio Moneta, 2005. "Does the Yield Spread Predict Recessions in the Euro Area?," International Finance, Wiley Blackwell, vol. 8(2), pages 263-301, August.
    5. Kholodilin, Konstantin A. & Yao, Vincent W., 2005. "Measuring and predicting turning points using a dynamic bi-factor model," International Journal of Forecasting, Elsevier, vol. 21(3), pages 525-537.
    6. Eraslan, Sercan & Nöller, Marvin, 2020. "Recession probabilities falling from the STARs," Discussion Papers 08/2020, Deutsche Bundesbank.
    7. Kholodilin Konstantin A., 2005. "Forecasting the German Cyclical Turning Points: Dynamic Bi-Factor Model with Markov Switching," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(6), pages 653-674, December.
    8. Konstantin A. Kholodilin, 2005. "Forecasting the Turns of German Business Cycle: Dynamic Bi-factor Model with Markov Switching," Discussion Papers of DIW Berlin 494, DIW Berlin, German Institute for Economic Research.
    9. Harm Bandholz, 2005. "New Composite Leading Indicators for Hungary and Poland," ifo Working Paper Series 3, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    10. Cem Çakmakli & Hamza Dem I˙rcani & Sumru Altug, 2021. "Modelling of Economic and Financial Conditions for Real‐Time Prediction of Recessions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 663-685, June.
    11. Konstantin A. Kholodilin, 2006. "Using the Dynamic Bi-Factor Model with Markov Switching to Predict the Cyclical Turns in the Large European Economies," Discussion Papers of DIW Berlin 554, DIW Berlin, German Institute for Economic Research.
    12. Morais, Igor Alexandre C. & Chauvet, Marcelle, 2011. "Leading Indicators for the Capital Goods Industry," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 31(1), March.
    13. Nissilä, Wilma, 2020. "Probit based time series models in recession forecasting – A survey with an empirical illustration for Finland," BoF Economics Review 7/2020, Bank of Finland.
    14. Issler, Joao Victor & Notini, Hilton & Rodrigues, Claudia & Soares, Ana Flávia, 2013. "Constructing coincident indices of economic activity for the Latin American economy," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 67(1), April.
    15. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2020. "Markov-Switching Three-Pass Regression Filter," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 285-302, April.
    16. Louise Holm, 2016. "The Swedish business cycle, 1969-2013," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2015(2), pages 1-22.
    17. repec:fgv:epgrbe:v:67:n:1:a:4 is not listed on IDEAS
    18. Chauvet, Marcelle & Piger, Jeremy, 2008. "A Comparison of the Real-Time Performance of Business Cycle Dating Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 42-49, January.
    19. Catherine Doz & Laurent Ferrara & Pierre-Alain Pionnier, 2020. "Business cycle dynamics after the Great Recession: An Extended Markov-Switching Dynamic Factor Model," Working Papers halshs-02443364, HAL.
    20. Haase, Felix & Neuenkirch, Matthias, 2023. "Predictability of bull and bear markets: A new look at forecasting stock market regimes (and returns) in the US," International Journal of Forecasting, Elsevier, vol. 39(2), pages 587-605.
    21. Michael T. Owyang & Jeremy Piger & Howard J. Wall, 2005. "Business Cycle Phases in U.S. States," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 604-616, November.
    22. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.

    More about this item

    Keywords

    composite economic indicator; Markov switching; smooth transition autoregression; turning points; reference cycle; forecasting;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

    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:ctl:louvir:2003008. 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: Virginie LEBLANC (email available below). General contact details of provider: https://edirc.repec.org/data/iruclbe.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.