Novel Semi-parametric Tobit Additive Regression Models

TL;DR

This paper introduces semi-parametric Tobit additive regression models that replace linear components with nonparametric additive functions, providing a flexible approach to handle censored data with efficient estimation methods.

Contribution

It extends traditional Tobit models to a semi-parametric framework with nonparametric additive components and proposes a computationally efficient likelihood-based estimation method.

Findings

Estimation method performs well in finite samples.

Method is computationally efficient and easy to implement.

Numerical experiments validate the model's effectiveness.

Abstract

Regression method has been widely used to explore relationship between dependent and independent variables. In practice, data issues such as censoring and missing data often exist. When the response variable is (fixed) censored, Tobit regression models have been widely employed to explore the relationship between the response variable and covariates. In this paper, we extend conventional parametric Tobit models to a novel semi-parametric regression model by replacing the linear components in Tobit models with nonparametric additive components, which we refer as Tobit additive models, and propose a likelihood based estimation method for Tobit additive models. %The proposed estimation method is computational efficient and easy to implement. Numerical experiments are conducted to evaluate the finite sample performance. The estimation method works well in finite sample experiments, even when sample size is relative small.