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Found 18 papers, 9 papers with code
Inference with Mondrian Random Forests
Random forests are popular methods for classification and regression, and many different variants have been proposed in recent years.
On the Implicit Bias of Adam
In previous literature, backward error analysis was used to find ordinary differential equations (ODEs) approximating the gradient descent trajectory.
Context-Dependent Heterogeneous Preferences: A Comment on Barseghyan and Molinari (2023)
Barseghyan and Molinari (2023) give sufficient conditions for semi-nonparametric point identification of parameters of interest in a mixture model of decision-making under risk, allowing for unobserved heterogeneity in utility functions and limited consideration.
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.
On the Pointwise Behavior of Recursive Partitioning and Its Implications for Heterogeneous Causal Effect Estimation
Decision tree learning is increasingly being used for pointwise inference.
Uncertainty Quantification in Synthetic Controls with Staggered Treatment Adoption
We propose principled prediction intervals to quantify the uncertainty of a large class of synthetic control predictions (or estimators) in settings with staggered treatment adoption, offering precise non-asymptotic coverage probability guarantees.
Beta-Sorted Portfolios
Beta-sorted portfolios -- portfolios comprised of assets with similar covariation to selected risk factors -- are a popular tool in empirical finance to analyze models of (conditional) expected returns.
Attention Overload
We introduce an Attention Overload Model that captures the idea that alternatives compete for the decision maker's attention, and hence the attention that each alternative receives decreases as the choice problem becomes larger.
Regression Discontinuity Designs
Over the last two decades, statistical and econometric methods for RD analysis have expanded and matured, and there is now a large number of methodological results for RD identification, estimation, inference, and validation.
A Practical Introduction to Regression Discontinuity Designs: Foundations
In this Element and its accompanying Element, Matias D. Cattaneo, Nicolas Idrobo, and Rocio Titiunik provide an accessible and practical guide for the analysis and interpretation of Regression Discontinuity (RD) designs that encourages the use of a common set of practices and facilitates the accumulation of RD-based empirical evidence.
Methodology Econometrics Applications Computation
The Regression Discontinuity Design
This handbook chapter gives an introduction to the sharp regression discontinuity design, covering identification, estimation, inference, and falsification methods.
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).
Simple Local Polynomial Density Estimators
This paper introduces an intuitive and easy-to-implement nonparametric density estimator based on local polynomial techniques.
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.
Extrapolating Treatment Effects in Multi-Cutoff Regression Discontinuity Designs
In non-experimental settings, the Regression Discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference.
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.
