L-Estimation Approach to Tobit Models with Endogeneity and Weakly Dependent Errors

TL;DR

This paper proposes an L-estimator for semiparametric Tobit models with endogenous regressors, addressing endogeneity and dependence issues, and demonstrates its effectiveness through simulations and real data analysis.

Contribution

It introduces a novel two-stage L-estimation method for Tobit models with endogenous regressors and weakly dependent errors, with proven large-sample properties.

Findings

Estimator performs well in simulations

Method effectively handles endogeneity

Applicable to real-world data sets

Abstract

This article introduces an L-estimator for the semiparametric Tobit model with endogenous regressors. The estimation procedure follows a two-stage approach: the first stage employs least squares, while the second stage utilizes the L-estimation technique. We establish the large-sample properties of the proposed estimators under weakly dependent data. The utility of the proposed methodology is demonstrated for various simulated data and a benchmark real data set.