This paper considers the semiparametric estimation of binary choice sample selection models under a joint symmetry assumption. Our approaches overcome various drawbacks associated with existing estimators. In particular, our method provides root-n consistent estimators for both the intercept and...

In this paper estimation of sample selection models using experimental data is considered with some weak restriction imposed on the error distribution. Under a normality setting, the most popular approach is the two-step method proposed by Heckman (1979). But Heckman¡¯s approach relies on the...

This paper considers semiparametric estimation of a nonstationary transformation model with panel data. One of the drawbacks of most existing semiparametric procedures is the requirement of stationarity assumption. Under a symmetry condition, a new estimator is proposed, allowing for...

We propose an estimation procedure for a semiparametric panel data censored regression model in which the error terms may be subject to general forms of nonstationarity. Specifically, we allow for heteroskedasticity over time and a time varying factor load on the individual specific effect....

We quantify the identifying power of special regressors in heteroskedastic binary regressions with median-independent or conditionally symmetric errors. We measure the identifying power using two criteria: the set of regressor values that help point identify coefficients in latent payoffs as in...

The Box–Cox regression model has been widely used in applied economics. However, there has been very limited discussion when data are censored. The focus has been on parametric estimation in the cross-sectional case, and there has been no discussion at all for the panel data model with fixed...

We consider censored structural latent variables models where some exogenous variables are subject to additive measurement errors. We demonstrate that overidentification conditions can be exploited to provide natural instruments for the variables measured with errors, and we propose a two-stage...

This paper proposes a new semiparametric estimator for the truncated regression model under the independence restriction. Many existing approaches such as those in Lee (1992) and Honoré and Powell (1994) are moment-based methods, whereas our approach makes use of the entire truncated...

The existing semiparametric estimation literature has mainly focused on univariate Tobit models and no semiparametric estimation has been considered for bivariate Tobit models. In this paper, we consider semiparametric estimation of the bivariate Tobit model proposed by Amemiya (1974), under the...