arXiv subjects

Joseph Fry

Publications and source records attributed to Joseph Fry.

3 recordsLinked to original sources

arXiv2026

Uniformly Valid Inference Under Interactive and High-Dimensional Constraints

Asymptotic normality approximations often fail to hold for extremum estimators when the true value of the parameter is at or close to the boundary of a parameter space. I analyze and develop tests using a quasi-unconstrained estimator, which is asymptotically normal even when the true parameter vector is near or at the boundary. These results generalize previous work with this estimator by allowing for more types of constraints and showing how the method can naturally be modified when a nuisance parameter is also high-dimensional. I show that variations of Wald, Likelihood Ratio, and Lagrange Multiplier tests can control size in a uniform sense, provided the initial constrained estimator is sufficiently accurate. Lastly, I apply the method to an application involving network estimation with panel data.

econ.EM↗

arXiv2025

Orthogonalized Synthetic Controls

When conducting inference for the average treatment effect on the treated with a Synthetic Control Estimator, the vector of control weights is a nuisance parameter that is often constrained, high-dimensional, and may be only partially identified even when the average treatment effect on the treated is point-identified. All three of these features of a nuisance parameter can lead to failure of asymptotic normality for the estimate of the parameter of interest when using standard methods. I provide a new method that yields asymptotic normality for an estimate of average treatment effects, even when all three complications are present. This is accomplished by first estimating the control weights and any other nuisance parameters using a regularization penalty to achieve identification, and then estimating average treatment effects using moment conditions that are orthogonalized with respect to the nuisance parameters. Additionally, I extend results from the fixed-smoothing literature to provide tests that control size without requiring consistent standard errors. I present high-level sufficient conditions applicable to the traditional Synthetic Control Estimator as well as other weighting-based panel data methods, and verify them in an example involving instrumental variables.

econ.EM↗

arXiv2023

A Method of Moments Approach to Asymptotically Unbiased Synthetic Controls

A common approach to constructing a Synthetic Control unit is to fit on the outcome variable and covariates in pre-treatment time periods, but it has been shown by Ferman and Pinto (2019) that this approach does not provide asymptotic unbiasedness when the fit is imperfect and the number of controls is fixed. Many related panel methods have a similar limitation when the number of units is fixed. I introduce and evaluate a new method in which the Synthetic Control is constructed using a General Method of Moments approach where units not being included in the Synthetic Control are used as instruments. I show that a Synthetic Control Estimator of this form will be asymptotically unbiased as the number of pre-treatment time periods goes to infinity, even when pre-treatment fit is imperfect and the number of units is fixed. Furthermore, if both the number of pre-treatment and post-treatment time periods go to infinity, then averages of treatment effects can be consistently estimated. I conduct simulations and an empirical application to compare the performance of this method with existing approaches in the literature.

econ.EM↗