Andreas Hagemann

Assistant Professor
Department of Economics
University of Michigan
Andreas Hagemann


Placebo inference on treatment effects when the number of clusters is small

PDF | arXiv | Revise and resubmit at the Journal of Econometrics

February 2018

I introduce a general, Fisher-style randomization testing framework to conduct nearly exact inference about the lack of effect of a binary treatment in the presence of very few, large clusters when the treatment effect is identified across clusters. The proposed randomization test formalizes and extends the intuitive notion of generating null distributions by assigning placebo treatments to untreated clusters. I show that under simple and easily verifiable conditions, the placebo test leads to asymptotically valid inference in a very large class of empirically relevant models. Examples discussed explicitly are (i) least squares regression with cluster-level treatment, (ii) difference-in-differences estimation, and (iii) binary choice models with cluster-level treatment. A simulation study and an empirical example are provided. The proposed inference procedure is easy to implement and performs well with as few as three treated and three untreated clusters.

Cluster-robust bootstrap inference in quantile regression models

PDF | arXiv | Journal of the American Statistical Association 112(517), pp. 446-456

May 2017

In this paper I develop a wild bootstrap procedure for cluster-robust inference in linear quantile regression models. I show that the bootstrap leads to asymptotically valid inference on the entire quantile regression process in a setting with a large number of small, heterogeneous clusters and provides consistent estimates of the asymptotic covariance function of that process. The proposed bootstrap procedure is easy to implement and performs well even when the number of clusters is much smaller than the sample size. An application to Project STAR data is provided.

The effect of college education on mortality

with K. Buckles, O. Malamud, M. Morrill, A. Wozniak
PDF | NBER | Journal of the Health Economics 50, pp. 99-114

December 2016

We exploit exogenous variation in college completion induced by draft-avoidance behavior during the Vietnam War to examine the impact of college completion on adult mortality. Our preferred estimates imply that increasing college completion rates from the level of the state with the lowest induced rate to the highest would decrease cumulative mortality by 28 percent relative to the mean. Most of the reduction in mortality is from deaths due to cancer and heart disease. We also explore potential mechanisms, including differential earnings, health insurance, and health behaviors, using data from the Census, ACS and NHIS.

Stochastic equicontinuity in nonlinear time series models

PDF | arXiv | Econometrics Journal 17(1), pp.188-196

February 2014

In this paper I provide simple and easily verifiable conditions under which a strong form of stochastic equicontinuity holds in a wide variety of modern time series models. In contrast to most results currently available in the literature, my methods avoid mixing conditions. I discuss several applications in detail.

Robust spectral analysis

PDF | arXiv | SSRN

December 2012

In this paper I introduce quantile spectral densities that summarize the cyclical behavior of time series across their whole distribution by analyzing periodicities in quantile crossings. This approach can capture systematic changes in the impact of cycles on the distribution of a time series and allows robust spectral estimation and inference in situations where the dependence structure is not accurately captured by the auto-covariance function. I study the statistical properties of quantile spectral estimators in a large class of nonlinear time series models and discuss inference both at fixed and across all frequencies. Monte Carlo experiments illustrate the advantages of quantile spectral analysis over classical methods when standard assumptions are violated.

A simple test for regression specification with non-nested alternatives

PDF | Journal of Econometrics 166(2) (2012), pp. 247-254

January 2012

In this paper, I introduce a simple test for the presence of the data-generating process among several non-nested alternatives. The test is an extension of the classical J test for non-nested regression models. I also provide a bootstrap version of the test that avoids possible size distortions inherited from the J test.

In progress

Fisher permutation inference with a finite number of heterogeneous clusters

PDF coming soon

November 2017


Econometrics Seminar


Fall 2016
Organized jointly with Matias Cattaneo and Lutz Kilian.

Mailing address

238 Lorch Hall
611 Tappan Ave
Ann Arbor, MI 48109-1220

Office address

351C Lorch Hall
611 Tappan Ave
Ann Arbor, MI 48109-1220