60 citations · 87 across the 11 of their papers we have counts for
6 papers · 1 filter
Bootstrap-Assisted Inference for Generalized Grenander-type Estimators
Matias D. Cattaneo, Michael Jansson, Kenichi Nagasawa
Westling and Carone (2020) proposed a framework for studying the large sample distributional properties of generalized Grenander-type estimators, a versatile class of nonparametric…
Boundary Adaptive Local Polynomial Conditional Density Estimators
Matias D. Cattaneo, Rajita Chandak, Michael Jansson +1
We begin by introducing a class of conditional density estimators based on local polynomial techniques. The estimators are boundary adaptive and easy to implement. We then study th…
Towards a General Large Sample Theory for Regularized Estimators
Michael Jansson, Demian Pouzo
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…
Bootstrap-Based Inference for Cube Root Asymptotics
Matias D. Cattaneo, Michael Jansson, Kenichi Nagasawa
This paper proposes a valid bootstrap-based distributional approximation for M-estimators exhibiting a Chernoff (1964)-type limiting distribution. For estimators of this kind, the…
Inference in Linear Regression Models with Many Covariates and Heteroskedasticity
Matias D. Cattaneo, Michael Jansson, Whitney K. Newey
The linear regression model is widely used in empirical work in Economics, Statistics, and many other disciplines. Researchers often include many covariates in their linear model s…
Alternative Asymptotics and the Partially Linear Model with Many Regressors
Matias D. Cattaneo, Michael Jansson, Whitney K. Newey
Non-standard distributional approximations have received considerable attention in recent years. They often provide more accurate approximations in small samples, and theoretical i…