We introduce an approach for semiparametric inference in dynamic binary choice models that does not impose distributional assumptions on the state variables unobserved by the econometrician. The proposed framework combines Bayesian inference with partial identification results. The method is...

We introduce an approach for semi-parametric inference in dynamic binary choice models that does not impose distributional assumptions on the state variables unobserved by the econometrician. The proposed framework combines Bayesian inference with partial identification results. The method is...

Strong assumptions needed to correctly specify parametric binary choice probability models make them particularly vulnerable to misspecification. Semiparametric models provide a less restrictive approach with estimators that exhibit desirable asymptotic properties. This paper discusses the...

This paper proposes new ℓ1-penalized quantile regression estimators for panel data, which explicitly allows for individual heterogeneity associated with covariates. We conduct Monte Carlo simulations to assess the small sample performance of the new estimators and provide comparisons of new...

This paper presents estimates of key preference parameters of the Epstein and Zin (1989 , 1991) and Weil (1989) recursive utility model, evaluates the model’s ability to fit asset return data relative to other asset pricing models, and investigates the implications of such estimates for the...

This paper proposes a semiparametric estimator for spatial autoregressive (SAR) binary choice models in the context of panel data with fixed effects. The estimation procedure is based on the observational equivalence between distribution free models with a conditional median restriction and...