457 citations · 500 across the 14 of their papers we have counts for
15 papers
Local Asymptotics for Treatment Choice with Partial Identification
José Luis Montiel Olea, Chen Qiu, Jörg Stoye
We provide a new asymptotic framework to derive approximately optimal treatment assignments when sampling noise from data is compounded by fundamental uncertainty due to partial id…
A Simple Approximation to the Distribution of the Ridge Regression Estimator
José Luis Montiel Olea, Ryan Strong, Amilcar Velez +2
We present a simple Gaussian approximation to the finite-sample distribution of the classical ridge regression estimator. Our approximation captures the fact that, in finite sample…
Approximate Minimax Estimation of a Bounded Normal Mean via Stochastic Mirror Ascent
José Luis Montiel Olea, Ekaterina Zubova
This paper presents a computational approach to find an approximately minimax estimator for the classical Bounded Normal Mean problem. The suggested procedure is the Bayes estimato…
Decision Theory for the Archetype Discovery Problem
José Luis Montiel Olea, Amilcar Velez, Zhuoheng Xu +2
In the archetype discovery problem a researcher wants to summarize N heterogeneous policy effects of interest that vary over a discrete set of covariates. The goal is to partition…
Evaluating Counterfactual Policies Using Instruments
Michal Kolesár, José Luis Montiel Olea, Jonathan Roth
We study settings in which a researcher has an instrumental variable (IV) and seeks to evaluate the effects of a counterfactual policy that alters treatment assignment, such as a d…
Approximate Least-Favorable Distributions and Nearly Optimal Tests via Stochastic Mirror Descent
Andrés Aradillas Fernández, José Blanchet, José Luis Montiel Olea +3
We consider a class of hypothesis testing problems where the null hypothesis postulates distributions for the observed data, and there is only one possible distribution under t…
Epsilon-Minimax Solutions of Statistical Decision Problems
Andrés Aradillas Fernández, José Blanchet, José Luis Montiel Olea +3
A decision rule is epsilon-minimax if it is minimax up to an additive factor epsilon. We present an algorithm for provably obtaining epsilon-minimax solutions for a class of statis…
Projection Inference for set-identified SVARs
Bulat Gafarov, Matthias Meier, José Luis Montiel Olea
We study the properties of the classical \emph{projection} method to conduct simultaneous inference about the coefficients of the structural impulse-response function and their ide…
Local Projections or VARs? A Primer for Macroeconomists
José Luis Montiel Olea, Mikkel Plagborg-Møller, Eric Qian +1
What should applied macroeconomists know about local projection (LP) and vector autoregression (VAR) impulse response estimators? The two methods share the same estimand, but in fi…
Robust Bayes Treatment Choice with Partial Identification
Andrés Aradillas Fernández, José Luis Montiel Olea, Chen Qiu +2
We study a class of binary treatment choice problems with partial identification through the lens of robust (multiple prior) Bayesian analysis. We use a convenient set of prior dis…
The out-of-sample prediction error of the square-root-LASSO and related estimators
José Luis Montiel Olea, Cynthia Rush, Amilcar Velez +1
We study the classical problem of predicting an outcome variable, , using a linear combination of a -dimensional covariate vector, . We are interested in linear p…
On the Robustness to Misspecification of -Posteriors and Their Variational Approximations
Marco Avella Medina, José Luis Montiel Olea, Cynthia Rush +1
-posteriors and their variational approximations distort standard posterior inference by downweighting the likelihood and introducing variational approximation errors. We show t…
Machine Learning's Dropout Training is Distributionally Robust Optimal
Jose Blanchet, Yang Kang, Jose Luis Montiel Olea +2
This paper shows that dropout training in Generalized Linear Models is the minimax solution of a two-player, zero-sum game where an adversarial nature corrupts a statistician's cov…
Local Projection Inference is Simpler and More Robust Than You Think
José Luis Montiel Olea, Mikkel Plagborg-Møller
Applied macroeconomists often compute confidence intervals for impulse responses using local projections, i.e., direct linear regressions of future outcomes on current covariates.…
Competing Models
Jose Luis Montiel Olea, Pietro Ortoleva, Mallesh M Pai +1
Different agents need to make a prediction. They observe identical data, but have different models: they predict using different explanatory variables. We study which agent believe…