Adam McCloskey

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

• 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…

  PublisherOA PDFDOI

• Inference for Losers

  AEA Papers and Proceedings · 2022 · 5 citations

  Senior authorCorresponding

  Researchers frequently report league tables ranking units (neighborhoods or firms, for instance) based on estimated coefficients. Since the rankings are formed based on estimates, however, the coefficients reported in league tables suffer from selection bias, with estimates for highly ranked units biased upward and those for low-ranked units biased downward. Further, conventional confidence intervals can undercover. This paper introduces corrected estimators and confidence intervals that address…

  PublisherDOI

• 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…

  PublisherDOI

• 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 un…

  PublisherOA PDFDOI

• Short and Simple Confidence Intervals when the Directions of Some\n Effects are Known

  arXiv (Cornell University) · 2021-09-16 · 1 citations

  preprintOpen accessSenior author

  We provide adaptive confidence intervals on a parameter of interest in the\npresence of nuisance parameters when some of the nuisance parameters have known\nsigns. The confidence intervals are adaptive in the sense that they tend to be\nshort at and near the points where the nuisance parameters are equal to zero.\nWe focus our results primarily on the practical problem of inference on a\ncoefficient of interest in the linear regression model when it is unclear\nwhether or not it is necessary to…

  PublisherOA PDFDOI

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

Similar researchers at University of Colorado Boulder

• Wolfgang Kellerh-53
• Miles Spencer Kimballh-45
• Yongmin Chenh-39