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A Classifying Procedure for Signaling Turning Points

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Author Info
Koskinen, Lasse (The Central Pension Security Institute)
Öller, Lars-Erik () (National Institute of Economic Research)

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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.

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Publisher Info
Paper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 427.

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Length: 22 pages
Date of creation: 07 Feb 2001
Date of revision:
Publication status: Published in Journal of Forecasting, 2004, pages 197-214.
Handle: RePEc:hhs:hastef:0427

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

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation

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  1. Gordon, Stephen, 1997. "Stochastic Trends, Deterministic Trends, and Business Cycle Turning Points," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(4), pages 411-34, July-Aug.. [Downloadable!]
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  2. Hamilton, James D & Perez-Quiros, Gabriel, 1996. "What Do the Leading Indicators Lead?," Journal of Business, University of Chicago Press, vol. 69(1), pages 27-49, January. [Downloadable!] (restricted)
    Other versions:
  3. 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 357-84, March. [Downloadable!] (restricted)
  4. Arturo Estrella & Frederic S. Mishkin, 1996. "Predicting U.S. recessions: financial variables as leading indicators," Research Paper 9609, Federal Reserve Bank of New York. [Downloadable!]
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  5. 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. [Downloadable!] (restricted)
  6. Francis X. Diebold & Glenn D. Rudebusch, 1988. "A nonparametric investigation of duration dependence in the American business cycle," Working Paper Series / Economic Activity Section 90, Board of Governors of the Federal Reserve System (U.S.).
    Other versions:
  7. 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.
  8. 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. [Downloadable!] (restricted)
    Other versions:
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