Accounting for sample selection is a challenge not only for empirical researchers, but also for the agents populating our models. Yet most models abstract from these issues and assume that agents successfully tackle selection problems. We design an experiment where a person who understands...

We propose a general framework for regularization in M-estimation problems under time dependent (absolutely regular-mixing) data which encompasses many of the existing estimators. We derive non-asymptotic concentration bounds for the regularized M-estimator. The concentration rate exhibits a...

We present a general framework for studying regularized estimators; such estimators are pervasive in estimation problems wherein \plug-in" type estimators are either ill-defined or ill-behaved. Within this framework, we derive, under primitive conditions, consistency and a generalization of the...

We present a general framework for studying regularized estimators; i.e., estimation problems wherein “plug-in” type estimators are either ill-defined or ill-behaved. We derive primitive conditions that imply consistency and asymptotic linear representation for regularized estimators,...

This paper establishes consistency of the weighted bootstrap for quadratic forms $( n^{-1/2} \sum_{i=1}^{n} Z_{i,n} )^{T}( n^{-1/2} \sum_{i=1}^{n} Z_{i,n})$ where $(Z_{i,n})_{i=1}^{n}$ are mean zero, independent $R^{d}$-valued random variables and $d=d(n)$ is allowed to grow with the sample size...