Andreas Petrou-Zeniou

I am a PhD student in economics at MIT. My research interests are in econometrics and empirical industrial organization. Currently, I am interested in understanding how researchers can make better decisions under statistical uncertainty, in settings ranging from ranking and selection problems, to market design and allocation problems. I graduated from the University of Chicago with degrees in economics and CAAM (concurrent MS) in 2024. My Erdős number is 5. 

My cv can be found here. 

Please feel free to reach out at apetrouz [at] mit [dot] edu

Working Papers:

Inference After Ranking with Applications to Economic Mobility [slides] (with Azeem Shaikh)

submitted

Abstract: This paper considers the problem of inference after ranking. In our setting, we are interested in any population whose rank according to some random quantity, such as an estimated treatment effect, a measure of value-added, or benefit (net of cost), falls in a pre-specified range of values. As such, this framework generalizes the inference on winners setting previously considered in Andrews, Kitagawa, and McCloskey (2024), in which a winner is understood to be the single population whose rank according to some random quantity is highest. We show that this richer setting accommodates a broad variety of empirically-relevant applications. We develop a two-step method for inference, which we compare to existing methods or their natural generalizations to this setting. We first show the finite-sample validity of this method in a normal location model and then develop asymptotic counterparts to these results by proving uniform validity over a large class of distributions satisfying a weak uniform integrability condition. Importantly, our results permit degeneracy in the covariance matrix of the limiting distribution, which arises naturally in many applications. In an application to the literature on economic mobility, we find that it is difficult to distinguish between high and low-mobility census tracts when correcting for selection. Finally, we demonstrate the practical relevance of our theoretical results through an extensive set of simulations.

Uncertainty in Compound Decisions 

Abstract: Researchers and policymakers frequently make a large number of decisions simultaneously over many units using noisy estimates of unit-level effects. To make optimal decisions, researchers often assume that unit-level effects are drawn from a common effect distribution. In applications, however, this effect distribution is unknown to the econometrician, introducing uncertainty in the ultimate decision-making problem. This paper considers the problem of quantifying this uncertainty. For instance, researchers may seek to understand how an oracle decision-maker with knowledge of the effect distribution would compare different feasible decisions, or understand which decisions an oracle would plausibly make. Analyzing these questions from the perspective of an oracle decision-maker involves comparing the conditional means of unit-level effects given estimates thereof, suggesting a sufficient inferential objective of uniform coverage of these conditional means. Based on an equivalence between uniform coverage of conditional means and multiple testing, I develop methods that satisfy uniform coverage. Finally, I consider two applications: one to the problem of identifying high-economic-mobility neighborhoods, and the other to auditing discriminatory firms. In both applications, my methods sharpen inference relative to existing methods and identify units that can be confidently included in oracle selections.

Works In Progress: