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…