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  5. The Econometrica Session: Developments in Econometrics
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The Econometrica Session: Developments in Econometrics

Paper Session

Saturday, Jan. 6, 2024 2:30 PM - 4:30 PM (CST)

Grand Hyatt, Presidio C
Hosted By: Econometric Society
  • Chair: Guido Imbens, Stanford University

Inference on Weighted Average Welfare with High-Dimensional State Space

Vira Semenova
,
University of California-Berkeley
Victor Chernozhukov
,
Massachusetts Institute of Technology
Whitney Newey Jr.
,
Massachusetts Institute of Technology

Abstract

This paper gives a consistent, asymptotically normal estimator of the expected value function when the state space is high-dimensional and the first-stage nuisance functions are estimated by modern machine learning tools. First, we show that value function is orthogonal to the conditional choice probability, therefore, this nuisance function needs to be estimated only at n−1/4 rate. Second, we give a correction term for the transition density of the state variable. The resulting orthogonal moment is robust to misspecification of the transition density and does not require this nuisance function to be consistently estimated. Third, we generalize this result by considering the weighted expected value. In this case, the orthogonal moment is doubly robust in the transition density and additional second-stage nuisance functions entering the correction term. We complete the asymptotic theory by providing bounds on second-order asymptotic terms.

Adaptation and Uncertainty Quantification

Timothy Buck Armstrong
,
University of Southern California

Abstract

Empirical research typically involves a robustness-eciency tradeo↵. A researcher seeking to estimate a scalar parameter can invoke strong assumptions to motivate a restricted estimator that is precise but may be heavily biased, or they can relax some of these assumptions to motivate a more robust, but variable, unrestricted estimator. When a bound on the bias of the restricted estimator is available, it is optimal to shrink the unrestricted estimator towards the restricted estimator. For settings where a bound on the bias of the restricted estimator is unknown, we propose adaptive shrinkage estimators that minimize the percentage increase in worst case risk relative to an oracle that knows the bound. We show that adaptive estimators solve a weighted convex minimax problem and provide lookup tables facilitating their rapid computation. Revisiting four empirical studies where questions of model specification arise, we examine the advantages of adapting to—rather than testing for—misspecification.

Design-Based Estimation of Structural Parameters, with an Application to Demand

Kirill Borusyak
,
University of California-Berkeley
Peter Hull
,
Brown University

Abstract

We develop and apply a general framework for using shocks from natural experiments to estimate key parameters in a class of structural models, via recentered instruments that exploit knowledge of the shock assignment process. This design-based identification approach imposes no restrictions on how model unobservables relate to predetermined variables, and yields a new class of optimal instruments. We show how instrument recentering relaxes strong assumptions in models of the demand for differentiated products.

Identifying Socially Disruptive Policies

Eric James Auerbach
,
Northwestern University

Abstract

Social disruption occurs when a policy creates or destroys many network connections between agents. It is a costly side effect of many interventions and so a growing empirical literature recommends measuring and accounting for social disruption when evaluating the welfare impact of a policy. However, there is currently little work characterizing what can actually be learned about social disruption from data in practice. In this paper, we consider the problem of identifying social disruption in a research design that is popular in the literature. We provide two sets of identification results. First, we show that social disruption is not generally point identified, but informative bounds can be constructed using the eigenvalues of the network adjacency matrices observed by the researcher. Second, we show that point identification follows from a theoretically motivated monotonicity condition, and we derive a closed form representation. We apply our methods in two empirical illustrations and find large policy effects that otherwise might be missed by alternatives in the literature.

Discussant(s)
Bruno Ferman
,
Getúlio Vargas Foundation
Liyang Sun
,
University College London and CEMFI
Pedro H.C. Sant'Anna
,
Emory University
Max Cytrynbaum
,
Yale University
JEL Classifications
  • C0 - General
  • O4 - Economic Growth and Aggregate Productivity