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Found 15 papers, 10 papers with code
The Fragility of Sparsity
We show, using three empirical applications, that linear regression estimates which rely on the assumption of sparsity are fragile in two ways.
Labour by Design: Contributions of David Card, Joshua Angrist, and Guido Imbens
The 2021 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel was awarded to David Card "for his empirical contributions to labour economics" and to Joshua Angrist and Guido Imbens "for their methodological contributions to the analysis of causal relationships."
When Can We Ignore Measurement Error in the Running Variable?
In many applications of regression discontinuity designs, the running variable used by the administrator to assign treatment is only observed with error.
One Instrument to Rule Them All: The Bias and Coverage of Just-ID IV
We revisit the finite-sample behavior of single-variable just-identified instrumental variables (just-ID IV) estimators, arguing that in most microeconometric applications, the usual inference strategies are likely reliable.
Contamination Bias in Linear Regressions
We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias.
Bias-Aware Inference in Regularized Regression Models
We consider inference on a scalar regression coefficient under a constraint on the magnitude of the control coefficients.
Robust Empirical Bayes Confidence Intervals
We construct robust empirical Bayes confidence intervals (EBCIs) in a normal means problem.
Sensitivity Analysis using Approximate Moment Condition Models
We show that near-optimal confidence intervals (CIs) can be formed by taking a generalized method of moments (GMM) estimator, and adding and subtracting the standard error times a critical value that takes into account the potential bias from misspecification of the moment conditions.
Econometrics Methodology
Shift-Share Designs: Theory and Inference
We study inference in shift-share regression designs, such as when a regional outcome is regressed on a weighted average of sectoral shocks, using regional sector shares as weights.
Finite-Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness
We consider estimation and inference on average treatment effects under unconfoundedness conditional on the realizations of the treatment variable and covariates.
Applications Econometrics Methodology
Inference in Regression Discontinuity Designs with a Discrete Running Variable
We consider inference in regression discontinuity designs when the running variable only takes a moderate number of distinct values.
Applications Methodology
Simple and Honest Confidence Intervals in Nonparametric Regression
We show that using the bandwidth that minimizes the maximum mean-squared error results in CIs that are nearly efficient and that in this case, the critical value depends only on the rate of convergence.
Applications Statistics Theory Statistics Theory
Optimal inference in a class of regression models
When the function class is centrosymmetric, these efficiency bounds imply that minimax CIs are close to efficient at smooth regression functions.
Statistics Theory Applications Statistics Theory
Minimum Distance Approach to Inference with Many Instruments
With respect to a particular weight matrix, the minimum distance estimator is equivalent to the random effects estimator of Chamberlain and Imbens (2004), and the estimator of the coefficient on the endogenous regressor coincides with the limited information maximum likelihood estimator.
Applications
A Simple Adjustment for Bandwidth Snooping
This paper proposes a simple adjustment that gives correct coverage in such situations: replace the normal quantile with a critical value that depends only on the kernel and ratio of the maximum and minimum bandwidths the researcher has entertained.
Applications
