published in: Journal of Applied Econometrics, 2017, 32, 342-358
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 and existing penalized estimators in terms of quadratic loss. We apply the techniques to two empirical studies.
First, the new method is applied to the estimation of labor supply elasticities and we find evidence that positive substitution effects dominate negative wealth effects at the middle of the conditional distribution of hours. The overall effect tends to be larger at the lower tail, which suggests that changes in taxes have different effects across the response distribution. Second, we estimate consumer preferences for nutrients from a demand model using a large scanner dataset of household food purchases. We show that preferences for nutrients vary across the conditional distribution of expenditure and across genders, and emphasize the importance of fully capturing consumer heterogeneity in demand modeling. Both applications highlight the importance of estimating individual heterogeneity when designing economic policy.
We use cookies to provide you with an optimal website experience. This includes cookies that are necessary for the operation of the site as well as cookies that are only used for anonymous statistical purposes, for comfort settings or to display personalized content. You can decide for yourself which categories you want to allow. Please note that based on your settings, you may not be able to use all of the site's functions.
Cookie settings
These necessary cookies are required to activate the core functionality of the website. An opt-out from these technologies is not available.
In order to further improve our offer and our website, we collect anonymous data for statistics and analyses. With the help of these cookies we can, for example, determine the number of visitors and the effect of certain pages on our website and optimize our content.