Nonparametric Estimation of Dynamic Discrete Choice Models for Time Series Data

Byeong U. Park, Leopold Simar and Valentin Zelenyuk
Additional contact information
Byeong U. Park: Department of Statistics, Seoul National University

No WP062016, CEPA Working Papers Series from University of Queensland, School of Economics

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.

Keywords: Nonparametric; Dynamic Discrete Choice; Probit (search for similar items in EconPapers)
JEL-codes: C14 C22 C25 C44 (search for similar items in EconPapers)
Date: 2016-10
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Citations: View citations in EconPapers (1)

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https://economics.uq.edu.au/files/5043/WP062016.pdf (application/pdf)

Related works:
Journal Article: Nonparametric estimation of dynamic discrete choice models for time series data (2017)
Working Paper: Nonparametric estimation of dynamic discrete choice models for time series data (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:qld:uqcepa:116

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