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Found 8 papers, 6 papers with code
Higher-order Refinements of Small Bandwidth Asymptotics for Density-Weighted Average Derivative Estimators
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.
Deep Learning for Individual Heterogeneity: An Automatic Inference Framework
These functions are the key inputs into the finite-dimensional parameter of inferential interest.
On Binscatter
Binscatter is a popular method for visualizing bivariate relationships and conducting informal specification testing.
Binscatter Regressions
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).
Deep Neural Networks for Estimation and Inference
We establish novel rates of convergence for deep feedforward neural nets.
Characteristic-Sorted Portfolios: Estimation and Inference
We develop a general framework for portfolio sorting by casting it as a nonparametric estimator.
Optimal Bandwidth Choice for Robust Bias Corrected Inference in Regression Discontinuity Designs
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.
Coverage Error Optimal Confidence Intervals for Local Polynomial Regression
This paper studies higher-order inference properties of nonparametric local polynomial regression methods under random sampling.
