no code implementations • 31 Dec 2022 • Matias D. Cattaneo, Max H. Farrell, Michael Jansson, Ricardo Masini
The resulting inference procedures based on small bandwidth asymptotics were found to exhibit superior finite sample performance in simulations, but no formal theory justifying that empirical success is available in the literature.
no code implementations • 28 Oct 2020 • Max H. Farrell, Tengyuan Liang, Sanjog Misra
These functions are the key inputs into the finite-dimensional parameter of inferential interest.
1 code implementation • 25 Feb 2019 • Matias D. Cattaneo, Richard K. Crump, Max H. Farrell, Yingjie Feng
The first four commands implement point estimation and uncertainty quantification (confidence intervals and confidence bands) for canonical and extended least squares binscatter regression (binsreg) as well as generalized nonlinear binscatter regression (binslogit for Logit regression, binsprobit for Probit regression, and binsqreg for quantile regression).
2 code implementations • 25 Feb 2019 • Matias D. Cattaneo, Richard K. Crump, Max H. Farrell, Yingjie Feng
Binscatter is a popular method for visualizing bivariate relationships and conducting informal specification testing.
1 code implementation • 26 Sep 2018 • Max H. Farrell, Tengyuan Liang, Sanjog Misra
We establish novel rates of convergence for deep feedforward neural nets.
1 code implementation • 10 Sep 2018 • Matias D. Cattaneo, Richard K. Crump, Max H. Farrell, Ernst Schaumburg
We develop a general framework for portfolio sorting by casting it as a nonparametric estimator.
2 code implementations • 1 Sep 2018 • Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
The theoretical findings are illustrated with a Monte Carlo experiment and an empirical application, and the main methodological results are available in \texttt{R} and \texttt{Stata} packages.
3 code implementations • 4 Aug 2018 • Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell
This paper studies higher-order inference properties of nonparametric local polynomial regression methods under random sampling.