On a log-symmetric quantile tobit model applied to female labor supply data

Danúbia R. Cunha, Jose Angelo Divino and Helton Saulo

Journal of Applied Statistics, 2022, vol. 49, issue 16, 4225-4253

Abstract: The study of female labor supply has been a topic of relevance in the economic literature. Generally, the data are left-censored and the classic tobit model has been extensively used in the modeling strategy. This model, however, assumes normality for the error distribution and is not recommended for data with positive skewness, heavy-tails and heteroscedasticity, as is the case of female labor supply data. Moreover, it is well-known that the quantile regression approach accounts for the influences of different quantiles in the estimated coefficients. We take all these features into account and propose a parametric quantile tobit regression model based on quantile log-symmetric distributions. The proposed method allows one to model data with positive skewness (which is not suitable for the classic tobit model), to study the influence of the quantiles of interest, and to account for heteroscedasticity. The model parameters are estimated by maximum likelihood and a Monte Carlo experiment is performed to evaluate alternative estimators. The new method is applied to two distinct female labor supply data sets. The results indicate that the log-symmetric quantile tobit model fits better the data than the classic tobit model.

Date: 2022
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Working Paper: On a log-symmetric quantile tobit model applied to female labor supply data (2021)
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DOI: 10.1080/02664763.2021.1976120

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