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A Classifying Procedure for Signaling Turning Points

Author

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  • 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
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    Cited by:

    1. Michał Bernardelli & Mariusz Próchniak & Bartosz Witkowski, 2017. "Cycle and Income-Level Convergence in the EU Countries: An Identification of Turning Points Based on the Hidden Markov Models," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 47, pages 27-42.
    2. Klaus Abberger, 2004. "Nonparametric Regression and the Detection of Turning Points in the Ifo Business Climate," CESifo Working Paper Series 1283, CESifo.
    3. 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.
    4. Tan, Zhengxun & Liu, Juan & Chen, Juanjuan, 2021. "Detecting stock market turning points using wavelet leaders method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    5. Michal Bernardelli & Mariusz Prochniak & Bartosz Witkowski, 2017. "The application of hidden Markov models to the analysis of real convergence," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 17, pages 59-80.
    6. Michał Bernardelli & Mariusz Próchniak & Bartosz Witkowski, 2018. "Przydatność ukrytych modeli Markowa do oceny podobieństwa krajów w zakresie synchronizacji wahań cyklicznych i wyrównywania się poziomów dochodu," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 53, pages 77-96.
    7. 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.
    8. Guizzardi, Andrea & Stacchini, Annalisa, 2015. "Real-time forecasting regional tourism with business sentiment surveys," Tourism Management, Elsevier, vol. 47(C), pages 213-223.
    9. 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.
    10. 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.
    11. Ard Reijer & Andreas Johansson, 2019. "Nowcasting Swedish GDP with a large and unbalanced data set," Empirical Economics, Springer, vol. 57(4), pages 1351-1373, October.

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    More about this item

    Keywords

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

    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

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