About

Adam McCloskey is an Associate Professor and Associate Chair of Graduate Studies in the Department of Economics at the University of Colorado Boulder. He holds a PhD in Economics from Boston University, where he also earned his MA, and completed his BA at the University of Colorado Boulder. His research interests include nonstandard inference problems, inference after model selection, and weak and partial identification. He is engaged in advancing methodologies within econometrics, particularly in the areas of time series analysis. McCloskey's academic background and research focus contribute to his role in teaching and mentoring graduate students, as well as his participation in departmental activities.

Research topics

• Artificial Intelligence
• Computer Science
• Econometrics
• Statistics
• Economics
• Mathematics

Selected publications

• Identification, Estimation and Inference in High-Frequency Event Study Regressions

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

• Numerical Analysis of Test Optimality

  ArXiv.org · 2025-12-22

  articleOpen access

  In nonstandard testing environments, researchers often derive ad hoc tests with correct (asymptotic) size, but their optimality properties are typically unknown a priori and difficult to assess. This paper develops a numerical framework for determining whether an ad hoc test is effectively optimal - approximately maximizing a weighted average power criterion for some weights over the alternative and attaining a power envelope generated by a single weighted average power-maximizing test. Our approach uses nested optimization algorithms to approximate the weight function that makes an ad hoc test's weighted average power as close as possible to that of a true weighted average power-maximizing test, and we show the surprising result that the rejection probabilities corresponding to the latter form an approximate power envelope for the former. We provide convergence guarantees, discuss practical implementation and apply the method to the weak instrument-robust conditional likelihood ratio test and a recently-proposed test for when a nuisance parameter may be on or near its boundary.

  PublisherOA PDF

• Dynamic Local Average Treatment Effects in Time Series

  ArXiv.org · 2025-09-16

  preprintOpen access

  This paper discusses identification, estimation, and inference on dynamic local average treatment effects (LATEs) in instrumental variables (IVs) settings. First, we show that compliers--observations whose treatment status is affected by the instrument--can be identified individually in time series data using smoothness assumptions and local comparisons of treatment assignments. Second, we show that this result enables not only better interpretability of IV estimates but also direct testing of the exclusion restriction by comparing outcomes among identified non-compliers across instrument values. Third, we document pervasive weak identification in applied work using IVs with time series data by surveying recent publications in leading economics journals. However, we find that strong identification often holds in large subsamples for which the instrument induces changes in the treatment. Motivated by this, we introduce a method based on dynamic programming to detect the most strongly-identified subsample and show how to use this subsample to improve estimation and inference. We also develop new identification-robust inference procedures that focus on the most strongly-identified subsample, offering efficiency gains relative to existing full sample identification-robust inference when identification fails over parts of the sample. Finally, we apply our results to heteroskedasticity-based identification of monetary policy effects. We find that about 75% of observations are compliers (i.e., cases where the variance of the policy shifts up on FOMC announcement days), and we fail to reject the exclusion restriction. Estimation using the most strongly-identified subsample helps reconcile conflicting IV and GMM estimates in the literature.

  PublisherOA PDFDOI

• Numerical Analysis of Test Optimality

  HAL (Le Centre pour la Communication Scientifique Directe) · 2025-12-22

  preprintOpen access

  In nonstandard testing environments, researchers often derive ad hoc tests with correct (asymptotic) size, but their optimality properties are typically unknown a priori and difficult to assess. This paper develops a numerical framework for determining whether an ad hoc test is effectively optimal - approximately maximizing a weighted average power criterion for some weights over the alternative and attaining a power envelope generated by a single weighted average power-maximizing test. Our approach uses nested optimization algorithms to approximate the weight function that makes an ad hoc test's weighted average power as close as possible to that of a true weighted average power-maximizing test, and we show the surprising result that the rejection probabilities corresponding to the latter form an approximate power envelope for the former. We provide convergence guarantees, discuss practical implementation and apply the method to the weak instrument-robust conditional likelihood ratio test and a recently-proposed test for when a nuisance parameter may be on or near its boundary.

  PublisherOA PDFDOI

• Inference for Interval-Identified Parameters Selected from an Estimated Set

  arXiv (Cornell University) · 2024-03-01

  preprintOpen accessSenior author

  Interval identification of parameters such as average treatment effects, average partial effects and welfare is particularly common when using observational data and experimental data with imperfect compliance due to the endogeneity of individuals' treatment uptake. In this setting, the researcher is typically interested in a treatment or policy that is either selected from the estimated set of best-performers or arises from a data-dependent selection rule. In this paper, we develop new inference tools for interval-identified parameters chosen via these forms of selection. We develop three types of confidence intervals for data-dependent and interval-identified parameters, discuss how they apply to several examples of interest and prove their uniform asymptotic validity under weak assumptions.

  PublisherOA PDFDOI

• Identification and Estimation of Causal Effects in High-Frequency Event Studies

  arXiv (Cornell University) · 2024-06-21

  preprintOpen accessSenior author

  We provide precise conditions for nonparametric identification of causal effects by high-frequency event study regressions, which have been used widely in the recent macroeconomics, financial economics and political economy literatures. The high-frequency event study method regresses changes in an outcome variable on a measure of unexpected changes in a policy variable in a narrow time window around an event or a policy announcement (e.g., a 30-minute window around an FOMC announcement). We show that, contrary to popular belief, the narrow size of the window is not sufficient for identification. Rather, the population regression coefficient identifies a causal estimand when (i) the effect of the policy shock on the outcome does not depend on the other variables (separability) and (ii) the surprise component of the news or event dominates all other variables that are present in the event window (relative exogeneity). Technically, the latter condition requires the ratio between the variance of the policy shock and that of the other variables to be infinite in the event window. Under these conditions, we establish the causal meaning of the event study estimand corresponding to the regression coefficient and the consistency and asymptotic normality of the event study estimator. Notably, this standard linear regression estimator is robust to general forms of nonlinearity. We apply our results to Nakamura and Steinsson's (2018a) analysis of the real economic effects of monetary policy, providing a simple empirical procedure to analyze the extent to which the standard event study estimator adequately estimates causal effects of interest.

  PublisherOA PDFDOI

• Critical Values Robust to P-hacking

  The Review of Economics and Statistics · 2024-05-06 · 5 citations

  articleOpen access1st authorCorresponding

  Abstract P-hacking is prevalent in reality but absent from classical hypothesis-testing theory. We therefore build a model of hypothesis testing that accounts for p-hacking. From the model, we derive critical values such that, if they are used to determine significance, and if p-hacking adjusts to the new significance standards, then spurious significant results do not occur more often than intended. Because of p-hacking, such robust critical values are larger than classical critical values. In the model calibrated to medical science, the robust critical value is the classical critical value for the same test statistic but with one-fifth of the significance level.

  PublisherOA PDFDOI

• Short and Simple Confidence Intervals When the Directions of Some Effects Are Known

  The Review of Economics and Statistics · 2023-02-07 · 2 citations

  articleOpen accessSenior author

  Abstract We introduce adaptive confidence intervals on a parameter of interest in the presence of nuisance parameters, such as coefficients on control variables, with known signs. Our confidence intervals are trivial to compute and can provide significant length reductions relative to standard ones when the nuisance parameters are small. At the same time, they entail minimal length increases at any parameter values. We apply our confidence intervals to the linear regression model, prove their uniform validity, and illustrate their length properties in an empirical application to a factorial design field experiment and a Monte Carlo study calibrated to the empirical application.

  PublisherOA PDFDOI

• Hybrid confidence intervals for informative uniform asymptotic inference after model selection

  Biometrika · 2023-03-24 · 4 citations

  article1st authorCorresponding

  Abstract I propose a new type of confidence interval for correct asymptotic inference after using data to select a model of interest without assuming any model is correctly specified. This hybrid confidence interval is constructed by combining techniques from the selective inference and post-selection inference literatures to yield a short confidence interval across a wide range of data realizations. I show that hybrid confidence intervals have correct asymptotic coverage, uniformly over a large class of probability distributions that do not bound scaled model parameters. I illustrate the use of these confidence intervals in the problem of inference after using the lasso objective function to select a regression model of interest and provide evidence of their desirable length and coverage properties in small samples via a set of Monte Carlo experiments that entail a variety of different data distributions as well as an empirical application to the predictors of diabetes disease progression.

  PublisherDOI

• Replication Data for: 'Inference on Winners'

  Harvard Dataverse · 2023-08-26

  datasetOpen accessSenior author

  The programs replicate tables and figures from "Inference on Winners", by Andrews, Kitagawa, and McCloskey. Please see the Readme files for additional details.

  PublisherDOI

Recent grants

• Computational Methods for Inference in Nonstandard Testing Problems

  NSF · $184k · 2014–2017

Frequent coauthors

• Pascal Michaillat

  University of California, Santa Cruz

  8 shared

• Toru Kitagawa

  8 shared

• Isaiah Andrews

  Harvard University Press

  7 shared

• Sukjin Han

  7 shared

• Philipp Ketz

  Paris Jourdan Sciences Economiques

  6 shared

• Jonathan B. Hill

  University of North Carolina at Chapel Hill

  5 shared

• Pierre Perrón

  Boston University

  3 shared

• Chad Brown

  2 shared

Education

• Ph.D., Economics

  Boston University

• M.A., Economics

  Boston University

• B.A.

  University of Colorado Boulder