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Nonparametric estimation of dynamic discrete choice models for time series data

Author

Listed:
  • Park, Byeong U.
  • Simar, Leopold
  • Zelenyuk, Valentin

Abstract

The non-parametric quasi-likelihood method is generalized to the context of discrete choice models for time series data where dynamics is modelled via lags of the discrete dependent variable appearing among regressors. Consistency and asymptotic normality of the estimator for such models in the general case is derived under the assumption of stationarity with strong mixing condition. Monte Carlo examples are used to illustrate performance of the proposed estimator relative to the fully parametric approach. Possible applications for the proposed estimator may include modelling and forecasting of probabilities of whether a subject would get a positive response to a treatment, whether in the next period an economy would enter a recession, or whether a stock market will go down or up, etc.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Park, Byeong U. & Simar, Leopold & Zelenyuk, Valentin, 2017. "Nonparametric estimation of dynamic discrete choice models for time series data," LIDAM Reprints ISBA 2017011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2017011
    Note: In : Computational Statistics & Data Analysis, vol. 108, p. 97-120 (2017)
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    Citations

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    Cited by:

    1. 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.
    2. Zeneida Sohl-Donguines & Rosenie S. Coronado, 2025. "Patient Safety Culture and Attitudes and The Interprofessional Collaboration Among Healthcare Workers in A Government Hospital," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 12(6), pages 1823-1858, June.
    3. Mastromarco, Camilla & Simar, Leopold & Wilson, Paul, 2019. "Predicting Recessions: A New Measure of Output Gap as Predictor," LIDAM Discussion Papers ISBA 2019023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Truquet, Lionel, 2023. "Strong mixing properties of discrete-valued time series with exogenous covariates," Stochastic Processes and their Applications, Elsevier, vol. 160(C), pages 294-317.
    5. Toru Kitagawa & Weining Wang & Mengshan Xu, 2022. "Policy Choice in Time Series by Empirical Welfare Maximization," Papers 2205.03970, arXiv.org, revised Nov 2025.
    6. Tatiana Anopchenko & Olga Gorbaneva & Elena Lazareva & Anton Murzin & Gennady Ougolnitsky, 2019. "Modeling Public—Private Partnerships in Innovative Economy: A Regional Aspect," Sustainability, MDPI, vol. 11(20), pages 1-18, October.
    7. Toru Kitagawa & Weining Wang & Mengshan Xu, 2024. "Policy choice in time series by empirical welfare maximization," CeMMAP working papers 27/24, Institute for Fiscal Studies.
    8. Byeong U. Park & Léopold Simar & Valentin Zelenyuk, 2020. "Forecasting of recessions via dynamic probit for time series: replication and extension of Kauppi and Saikkonen (2008)," Empirical Economics, Springer, vol. 58(1), pages 379-392, January.
    9. Timothy Armstrong & Martin Weidner & Andrei Zeleneev, 2024. "Robust estimation and inference in panels with interactive fixed effects," IFS Working Papers WCWP28/24, Institute for Fiscal Studies.
    10. Qingyan Ning & Maosheng Li, 2022. "Modeling Pedestrian Detour Behavior By-Passing Conflict Areas," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    11. 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.

    More about this item

    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
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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