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Revisiting Forecasting of Recessions via Dynamic Probit for Time Series by Kauppi and Saikkonen (2008)

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Abstract

In this work we first replicate the results of 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 non-parametric local-likelihood dynamic probit model for the same data.

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  • Byeong U. Park & Leopold Simar & Valentin Zelenyuk, 2017. "Revisiting Forecasting of Recessions via Dynamic Probit for Time Series by Kauppi and Saikkonen (2008)," CEPA Working Papers Series WP032017, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:120
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    File URL: https://economics.uq.edu.au/files/5031/WP032017.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. Henderson,Daniel J. & Parmeter,Christopher F., 2015. "Applied Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9781107010253, Enero-Abr.
    4. 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.
    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.
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