IDEAS home Printed from https://ideas.repec.org/p/hhs/hastef/0427.html
   My bibliography  Save this paper

A Classifying Procedure for Signaling Turning Points

Author

Listed:
  • Koskinen, Lasse

    (The Central Pension Security Institute)

  • Öller, Lars-Erik

    () (National Institute of Economic Research)

Abstract

A Hidden Markov Model (HMM) is used to classify an out of sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points. Instead o maximizing a likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimation for different forecasting horizons. The inference aspect is emphasized by including a penalty for a wrong decision in the cost function. The method is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourable with earlier HMM studies. Some theoretical arguments for this are given.

Suggested Citation

  • Koskinen, Lasse & Öller, Lars-Erik, 2001. "A Classifying Procedure for Signaling Turning Points," SSE/EFI Working Paper Series in Economics and Finance 427, Stockholm School of Economics.
  • Handle: RePEc:hhs:hastef:0427
    as

    Download full text from publisher

    File URL: http://swopec.hhs.se/hastef/papers/hastef0427.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 45-61, February.
    2. Fintzen, David & Stekler, H. O., 1999. "Why did forecasters fail to predict the 1990 recession?," International Journal of Forecasting, Elsevier, vol. 15(3), pages 309-323, July.
    3. Gordon, Stephen, 1997. "Stochastic Trends, Deterministic Trends, and Business Cycle Turning Points," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(4), pages 411-434, July-Aug..
    4. Kontolemis, Zenon G, 2001. "Analysis of the US Business Cycle with a Vector-Markov-Switching Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(1), pages 47-61, January.
    5. Swanson, Norman R. & White, Halbert, 1997. "Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 439-461, December.
    6. Oller, Lars-Erik & Tallbom, Christer, 1996. "Smooth and timely business cycle indicators for noisy Swedish data," International Journal of Forecasting, Elsevier, vol. 12(3), pages 389-402, September.
    7. Artis, Michael J, et al, 1995. "Turning Point Prediction for the UK Using CSO Leading Indicators," Oxford Economic Papers, Oxford University Press, vol. 47(3), pages 397-417, July.
    8. 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.
    9. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
    10. Hamilton, James D & Perez-Quiros, Gabriel, 1996. "What Do the Leading Indicators Lead?," The Journal of Business, University of Chicago Press, vol. 69(1), pages 27-49, January.
    11. James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1.
    12. 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.
    13. Stock, James H. & Watson, Mark W. (ed.), 1993. "Business Cycles, Indicators, and Forecasting," National Bureau of Economic Research Books, University of Chicago Press, number 9780226774886.
    14. Ivanova, Detelina & Lahiri, Kajal & Seitz, Franz, 2000. "Interest rate spreads as predictors of German inflation and business cycles," International Journal of Forecasting, Elsevier, vol. 16(1), pages 39-58.
    15. Stéphane Grégoir & Fabrice Lenglart, 1998. "Measuring the Probability of a Business Cycle Turning Point by Using a Multivariate Qualitative Hidden Markov Model," Working Papers 98-48, Center for Research in Economics and Statistics.
    16. Layton, Allan P., 1996. "Dating and predicting phase changes in the U.S. business cycle," International Journal of Forecasting, Elsevier, vol. 12(3), pages 417-428, September.
    17. James H. Stock & Mark W. Watson, 1993. "Introduction to "Business Cycles, Indicators and Forecasting"," NBER Chapters,in: Business Cycles, Indicators and Forecasting, pages 1-10 National Bureau of Economic Research, Inc.
    18. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895, January.
    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. repec:sgh:annals:i:47:y:2017:p:27-42 is not listed on IDEAS
    2. repec:cpn:umkdem:v:17:y:2017:p:59-80 is not listed on IDEAS
    3. Hansson, Jesper & Jansson, Per & Lof, Marten, 2005. "Business survey data: Do they help in forecasting GDP growth?," International Journal of Forecasting, Elsevier, vol. 21(2), pages 377-389.
    4. repec:eee:touman:v:47:y:2015:i:c:p:213-223 is not listed on IDEAS
    5. Andersson, Eva, 2007. "Effect of dependency in systems for multivariate surveillance," Research Reports 2007:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    6. Chow, Hwee Kwan & Choy, Keen Meng, 2006. "Forecasting the global electronics cycle with leading indicators: A Bayesian VAR approach," International Journal of Forecasting, Elsevier, vol. 22(2), pages 301-315.
    7. Yun-Ling Wu & Cheng-Huang Tung & Chun-Chang Lee, 2017. "The Power of a Leading Indicators Fluctuation Trend for Forecasting Taiwans Real Estate Business Cycle: An Application of a Hidden Markov Model," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 7(1), pages 81-98, January.

    More about this item

    Keywords

    Business Cycle; Feature Extraction; Hidden Markov Switching-Regime Model; Leading Indicator; Probability Forecast.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    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:hastef:0427. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Helena Lundin). General contact details of provider: http://edirc.repec.org/data/erhhsse.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.