Amilcar Velez

Ph.D. Candidate in Economics

Northwestern University

Welcome!

I am a Ph.D. candidate in Economics at Northwestern University, originally from Puno, Peru, and currently on the 2024-2025 job market. My research interests lie broadly in econometric theory, (statistical) machine learning, and their applications in economics.

Northwestern Website: https://sites.northwestern.edu/amilcarvelez/

Interests

Econometrics
(Statistical) Machine Learning
Experiments
Statistical Decision Theory

Education

Ph.D. in Economics, 2019-present
Northwestern University
M.A. in Economics, 2020
Northwestern University
M.S. in Mathematics, 2015
Instituto Nacional de Matematica Pura e Aplicada
B.A. in Economics, 2014
Pontificie Universidad Catolica del Peru

Job Market Paper

On the Asymptotic Properties of Debiased Machine Learning Estimators

This paper studies the properties of debiased machine learning (DML) estimators under a novel asymptotic framework, offering insights for improving estimator performance in applications. DML is an estimation method suited to economic models in which the parameter of interest depends on unknown nuisance functions that must be estimated. It requires weaker conditions than previous methods while still ensuring standard asymptotic properties. Existing theoretical results do not distinguish between the two alternative versions of DML estimators, namely, DML1 and DML2. Under a new asymptotic framework, this paper demonstrates that DML2 asymptotically dominates DML1 in terms of bias and mean squared error, formalizing a previous conjecture based on the simulation results of their relative performance. Additionally, this paper provides guidance for improving DML2 performance in applications.

PDF Online Appendix

Last updated on Nov 18, 2024

Working papers

(feedback is welcome!)

On the Power Properties of Confidence Sets for Parameters with Interval Identified Sets

joint with Federico Bugni, Mengsi Gao, and Filip Obradovic. New version available!

Last updated on Oct 26, 2024

Identification and Inference on Treatment Effects under Covariate-Adaptive Randomization and Imperfect Compliance

joint with Federico Bugni, Mengsi Gao, and Filip Obradovic. New version available!

Last updated on Aug 30, 2024

The out-of-sample prediction error of the square-root-LASSO and related estimators

joint with José Luis Montiel Olea, Cynthia Rush, and Johannes Wiesel. Resubmitted.

Last updated on Sep 22, 2024

Linear IV Model with Missing Data on the Instrumental Variable

Draft coming soon

Last updated on Nov 4, 2024

The Local Projection Residual Bootstrap for AR(1) Models

New version available!

Last updated on Jan 5, 2024

Representation Learning in Linear Factor Models

joint with José Luis Montiel Olea.

Last updated on Dec 13, 2022

Publications

A User's Guide for Inference in Models Defined by Moment Inequalities

joint with Ivan A. Canay and Gaston Illanes. Accepted at Journal of Econometrics.

The A/B Testing Problem with Gaussian Priors

joint with Eduardo M. Azevedo, David Mao, and José Luis Montiel Olea. Journal of Economic Theory, Volume (210), June 2023.

On the Robustness to Misspecification of alpha-Posteriors and Their Variational Approximations

Joint with Marco Avella Medina, José Luis Montiel Olea, and Cynthia Rush. Journal of Machine Learning Research, 23(147):1−51, 2022.

Posterior distribution of nondifferentiable functions

Joint with Toru Kitagawa, José Luis Montiel Olea, and Jonathan Payne. Journal of Econometrics, Volume 217(1), July 2020, pp. 161-175. Supplementary Appendix (pdf).