Nonparametric estimation of dynamic discrete choice models for time series data

Byeong U. Park, Leopold Simar and Valentin Zelenyuk

Computational Statistics & Data Analysis, 2017, vol. 108, issue C, 97-120

Abstract: The non-parametric quasi-likelihood method is generalized to the context of discrete choice models for time series data, where the dynamic aspect is modeled 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 modeling 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 quasi-likelihood; Local-likelihood; Dynamic probit; Forecasting (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (13)

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Related works:
Working Paper: 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 (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:108:y:2017:i:c:p:97-120

DOI: 10.1016/j.csda.2016.10.024

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