Testing for asymmetric information in insurance markets has become a very important issue in the empirical literature in the last years. We analyze the (private) accident insurance, which has not been analyzed before in the literature, but covers one of the most important risks faced by...

As of December 21, 2012, the use of gender as an insurance rating category was prohibited. Any remaining pricing disparities between men and women will now be traced back to the reasonable pricing of characteristics that happen to differ between the groups or to the pricing of characteristics...

Since Chiappori and Salani e (2000), testing for asymmetric information has become tantamount to testing for a positive relation between risk and coverage which is predicted by (equilibrium) models for adverse selection and moral hazard. In this paper we analyze asymmetric information in...

This paper deals with LASSO regression in high-dimensional sparse linear models with time series data. We propose heteroskedasticity and autocorrelation consistent (HAC) and heteroskedasticity and autocorrelation robust (HAR) estimates for the penalty loadings and evaluate the in-sample fitting...

In this paper we analyze asymmetric information in the (private) disability insurance, which has not been analyzed before in the literature, but covers one of the most important risks faced by individuals in modern society, namely the loss of human capital. We show that there is asymmetric...

We present an expository, general analysis of valid post-selection or post-regularization inference about a low-dimensional target parameter in the presence of a very high-dimensional nuisance parameter that is estimated using selection or regularization methods. Our analysis provides a set of...

In this note, we offer an approach to estimating structural parameters in the presence of many instruments and controls based on methods for estimating sparse high-dimensional models. We use these high-dimensional methods to select both which instruments and which control variables to use. The...