60 citations · 87 across the 11 of their papers we have counts for
15 papers
Robust Inference for Convex Pairwise Difference Estimators
Matias D. Cattaneo, Michael Jansson, Kenichi Nagasawa
This paper develops distribution theory and bootstrap-based inference methods for a broad class of convex pairwise difference estimators. These estimators minimize a kernel-weighte…
Continuity of the Distribution Function of the argmax of a Gaussian Process
Matias D. Cattaneo, Gregory Fletcher Cox, Michael Jansson +1
Certain extremum estimators have asymptotic distributions that are non-Gaussian, yet characterizable as the distribution of the of a Gaussian process. This paper presents…
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…
Higher-order Refinements of Small Bandwidth Asymptotics for Density-Weighted Average Derivative Estimators
Matias D. Cattaneo, Max H. Farrell, Michael Jansson +1
The density weighted average derivative (DWAD) of a regression function is a canonical parameter of interest in economics. Classical first-order large sample distribution theory fo…
lpcde: Estimation and Inference for Local Polynomial Conditional Density Estimators
Matias D. Cattaneo, Rajita Chandak, Michael Jansson +1
This paper discusses the R package lpcde, which stands for local polynomial conditional density estimation. It implements the kernel-based local polynomial smoothing methods introd…
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…
Local Regression Distribution Estimators
Matias D. Cattaneo, Michael Jansson, Xinwei Ma
This paper investigates the large sample properties of local regression distribution estimators, which include a class of boundary adaptive density estimators as a prime example. F…
lpdensity: Local Polynomial Density Estimation and Inference
Matias D. Cattaneo, Michael Jansson, Xinwei Ma
Density estimation and inference methods are widely used in empirical work. When the underlying distribution has compact support, conventional kernel-based density estimators are n…
Average Density Estimators: Efficiency and Bootstrap Consistency
Matias D. Cattaneo, Michael Jansson
This paper highlights a tension between semiparametric efficiency and bootstrap consistency in the context of a canonical semiparametric estimation problem, namely the problem of e…
Simple Local Polynomial Density Estimators
Matias D. Cattaneo, Michael Jansson, Xinwei Ma
This paper introduces an intuitive and easy-to-implement nonparametric density estimator based on local polynomial techniques. The estimator is fully boundary adaptive and automati…
Two-Step Estimation and Inference with Possibly Many Included Covariates
Matias D. Cattaneo, Michael Jansson, Xinwei Ma
We study the implications of including many covariates in a first-step estimate entering a two-step estimation procedure. We find that a first order bias emerges when the number of…
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…