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Found 18 papers, 7 papers with code
Treatment Choice, Mean Square Regret and Partial Identification
We consider a decision maker who faces a binary treatment choice when their welfare is only partially identified from data.
SGMM: Stochastic Approximation to Generalized Method of Moments
We introduce a new class of algorithms, Stochastic Generalized Method of Moments (SGMM), for estimation and inference on (overidentified) moment restriction models.
A Robust Method for Microforecasting and Estimation of Random Effects
We propose a method for forecasting individual outcomes and estimating random effects in linear panel data models and value-added models when the panel has a short time dimension.
Prediction Risk and Estimation Risk of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors
In recent years, there has been a significant growth in research focusing on minimum $\ell_2$ norm (ridgeless) interpolation least squares estimators.
Individual Welfare Analysis: Random Quasilinear Utility, Independence, and Confidence Bounds
We introduce a novel framework for individual-level welfare analysis.
Implicit Bias against a Capitalistic Society Predicts Market Earnings
This paper investigates whether ideological indoctrination by living in a communist regime relates to low economic performance in a market economy.
Fast Inference for Quantile Regression with Tens of Millions of Observations
Big data analytics has opened new avenues in economic research, but the challenge of analyzing datasets with tens of millions of observations is substantial.
Average Adjusted Association: Efficient Estimation with High Dimensional Confounders
The log odds ratio is a well-established metric for evaluating the association between binary outcome and exposure variables.
Treatment Choice with Nonlinear Regret
The literature focuses on the mean of welfare regret, which can lead to undesirable treatment choice due to sensitivity to sampling uncertainty.
Bounding Treatment Effects by Pooling Limited Information across Observations
Our bounds are designed to be robust in challenging situations, for example, when the conditioning variables take on a large number of different values in the observed sample, or when the overlap condition is violated.
Why North Korean Refugees are Reluctant to Compete: The Roles of Cognitive Ability
The study compares the competitiveness of three Korean groups raised in different institutional environments: South Korea, North Korea, and China.
Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling
We develop a new method of online inference for a vector of parameters estimated by the Polyak-Ruppert averaging procedure of stochastic gradient descent (SGD) algorithms.
Inference for parameters identified by conditional moment restrictions using a generalized Bierens maximum statistic
Many economic panel and dynamic models, such as rational behavior and Euler equations, imply that the parameters of interest are identified by conditional moment restrictions with high dimensional conditioning instruments.
Treatment Effects with Targeting Instruments
Multivalued treatments are commonplace in applications.
Least Squares Estimation Using Sketched Data with Heteroskedastic Errors
The result arises because the sketched estimates in the case of random projections can be expressed as degenerate $U$-statistics, and under certain conditions, these statistics are asymptotically normal with homoskedastic variance.
Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions
We study causal inference under case-control and case-population sampling.
Identifying the Effect of Persuasion
This paper examines a commonly used measure of persuasion whose precise interpretation has been obscure in the literature.
High Dimensional Classification through $\ell_0$-Penalized Empirical Risk Minimization
We consider a high dimensional binary classification problem and construct a classification procedure by minimizing the empirical misclassification risk with a penalty on the number of selected features.
