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Forecasting of Recessions via Dynamic Probit for Time Series: Replication and Extension of Kauppi and Saikkonen (2008)

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In this work we first replicate the results of the fully parametric dynamic probit model for forecasting US recessions from Kauppi and Saikkonen (2008) (which is in the spirit of Estrella and Mishkin (1995, 1998) and Dueker (1997)) and then contrast them to results from a nonparametric local-likelihood dynamic choice model for the same data. We then use expanded data to gain insights on whether these models could have warned the public about approach of the latest recession, associated with the Global Financial Crisis. Finally, we also apply both approaches to gain insights for 2018.

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  • Byeong U. Park & Lèopold Simar & Valentin Zelenyuk, 2018. "Forecasting of Recessions via Dynamic Probit for Time Series: Replication and Extension of Kauppi and Saikkonen (2008)," CEPA Working Papers Series WP092018, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:130
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    File URL: https://economics.uq.edu.au/files/12172/WP092018.pdf
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    1. Li, Degui & Simar, Léopold & Zelenyuk, Valentin, 2016. "Generalized nonparametric smoothing with mixed discrete and continuous data," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 424-444.
    2. Park, Byeong U. & Simar, Léopold & Zelenyuk, Valentin, 2017. "Nonparametric estimation of dynamic discrete choice models for time series data," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 97-120.
    3. Peter Temin, 2010. "The Great Recession and the Great Depression," NBER Working Papers 15645, National Bureau of Economic Research, Inc.
    4. Henderson,Daniel J. & Parmeter,Christopher F., 2015. "Applied Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9781107010253.
    5. Estrella, Arturo, 1998. "A New Measure of Fit for Equations with Dichotomous Dependent Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 198-205, April.
    6. Chi-Yang Chu & Daniel J. Henderson & Christopher F. Parmeter, 2015. "Plug-in Bandwidth Selection for Kernel Density Estimation with Discrete Data," Econometrics, MDPI, vol. 3(2), pages 1-16, March.
    7. Anna Florio, 2004. "The Asymmetric Effects of Monetary Policy," Journal of Economic Surveys, Wiley Blackwell, vol. 18, pages 409-426, July.
    8. Anna Florio, 2004. "The Asymmetric Effects of Monetary Policy," Journal of Economic Surveys, Wiley Blackwell, vol. 18(3), pages 409-426, July.
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    Cited by:

    1. Hasse, Jean-Baptiste & Lajaunie, Quentin, 2022. "Does the yield curve signal recessions? New evidence from an international panel data analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 9-22.
    2. Camilla Mastromarco & Léopold Simar & Valentin Zelenyuk, 2021. "Predicting recessions with a frontier measure of output gap: an application to Italian economy," Empirical Economics, Springer, vol. 60(6), pages 2701-2740, June.

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    More about this item

    Keywords

    Forecasting of recessions; Nonparametric quasi-likelihood; Local likelihood; Dynamic discrete choice.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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