Here 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 which is estimated using selection or regularization methods. Our analysis provides a...

In this article the package High-dimensional Metrics (hdm) is introduced. It is a collection of statistical methods for estimation and quantification of uncertainty in high-dimensional approximately sparse models. It focuses on providing confidence intervals and significance testing for...

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...

Asymmetric information is an important phenomenon in insurance markets, but the empirical evidence on the extent of adverse selection and moral hazard is mixed. Because of its implications for pricing, contract design, and regulation, it is crucial to test for asymmetric information in specific...

Testing for asymmetric informations has become very important in recent years in order to test theoretical predictions and to outline new directions for research. In this paper we analyse accident insurance, which has not been analysed before in the literature, but covers one of the most...

Here we present an expository, general analysis of valid post-selection or post-regularization inference about a low-dimensional target parameter, α, in the presence of a very high-dimensional nuisance parameter, η, which is estimated using modern selection or regularization methods. Our...