About

Gary Chamberlain was a distinguished Professor of Economics at Harvard University and held the title of Louis Berkman Research Professor of Economics. He passed away in February 2020. His academic career included teaching at the University of Wisconsin-Madison before joining Harvard in 1987. He became the Louis Berkman Professor of Economics in 2002. His research encompassed a wide range of topics including panel data, returns to schooling, factor structure in large asset markets, semiparametric efficiency, the structure of wages, and applications of decision theory in econometrics. He was recognized for his contributions to the field by being elected a Fellow of the Econometric Society, serving on its Council from 1988 to 1993, and delivering the Fisher-Schultz Lecture in 2001. Additionally, he was a Fellow of the American Academy of Arts and Sciences, a Fellow of the American Association for the Advancement of Science, and a member of the National Academy of Sciences. Chamberlain graduated from Harvard College with an A.B. in 1970, Summa Cum Laude, and earned his Ph.D. in Economics from Harvard University in 1975.

Research topics

• Computer Science
• Econometrics
• Mathematics
• Economics
• Statistics
• Mathematical economics
• Financial economics
• Microeconomics

Selected publications

• Identification in dynamic binary choice models

  2023-07-26

  reportOpen access1st authorCorresponding

  This paper studies identification in a binary choice panel data model with choice probabilities depending on a lagged outcome, additional observed regressors and an unobserved unit-specific effect.It is shown that with two consecutive periods of data identification is not possible (in a neighborhood of zero), even in the logistic case.

  PublisherOA PDFDOI

• Identification in dynamic binary choice models

  SERIEs · 2023 · 3 citations

  1st authorCorresponding
  • Computer Science
  • Econometrics
  • Statistics

  Abstract This paper studies identification in a binary choice panel data model with choice probabilities depending on a lagged outcome, additional observed regressors and an unobserved unit-specific effect. It is shown that with two consecutive periods of data identification is not possible (in a neighborhood of zero), even in the logistic case.

  PublisherOA PDFDOI

• Unobservables in Economic Models

  Digital Access to Scholarship at Harvard (DASH) (Harvard University) · 2021-04-07

  dissertation1st authorCorresponding
  Publisher

• Feedback in panel data models

  Journal of Econometrics · 2021 · 21 citations

  1st authorCorresponding
  • Mathematics
  • Econometrics
  • Statistics
  PublisherDOI

• Feedback in panel data models

  2021-08-05 · 3 citations

  reportOpen access1st authorCorresponding

  Much of the analysis of panel data has been based on an assumption of strict exogeneity. Distributions are specified for outcome variables conditional on a latent individual effect and conditional on observed predictor variables at all dates, with the future values of the predictor variables assumed to have no effect on the conditional distribution. The paper relaxes this assumption in order to allow for lagged dependent variables and, more generally, for feedback from lagged dependent variables to current values of the predictor variables. Such feedback would arise in an evaluation study if the treatment variable is randomly assigned only conditional on the individual effect and on previous outcomes. An information bound is derived for a semiparametric regression model with sequential moment restrictions, with the information set increasing over time. The bound is then applied to a model with a (scalar) multiplicative random effect. The mean of the random effect conditional on the predictor variables is not restricted, so that the random effect can control for various omitted variables. This conditional mean is the nonparametric component of the semiparametric regression model. There is a transformation that eliminates the random effect and leads to a set of sequential moment restrictions in which the moment function depends on only a finite-dimensional parameter. The information bound for this simpler problem coincides with that of the original problem. The form of the optimal instrumental variables is derived. The paper also considers the identification problems that arise when the random effect is a vector with two or more components.

  PublisherOA PDFDOI

• Robust Decision Theory and Econometrics

  Annual Review of Economics · 2020 · 11 citations

  1st authorCorresponding
  • Computer Science
  • Economics
  • Econometrics

  This review uses the empirical analysis of portfolio choice to illustrate econometric issues that arise in decision problems. Subjective expected utility (SEU) can provide normative guidance to an investor making a portfolio choice. The investor, however, may have doubts on the specification of the distribution and may seek a decision theory that is less sensitive to the specification. I consider three such theories: maxmin expected utility, variational preferences (including multiplier and divergence preferences and the associated constraint preferences), and smooth ambiguity preferences. I use a simple two-period model to illustrate their application. Normative empirical work on portfolio choice is mainly in the SEU framework, and bringing in ideas from robust decision theory may be fruitful.

  PublisherDOI

• Fixed Effects, Invariance, and Spatial Variation in Intergenerational Mobility

  American Economic Review · 2016-05-01 · 3 citations

  article1st authorCorresponding

  Chetty et al. (2014) document variation across commuting zones in intergenerational mobility. With over 700 commuting zones, the task of estimating place effects involves a high-dimension parameter space. I develop a fixed-effects model along with an oracle bound on the risk of invariant estimators. The oracle estimator uses an invariant prior, which I have incorporated into a random-effects model to obtain a feasible estimator. This estimator almost achieves the oracle bound over the relevant part of the (fixed-effects) parameter space in the empirical application. There is substantial reduction in risk compared with the least-squares estimator.

  PublisherDOI

• Predictive effects of teachers and schools on test scores, college attendance, and earnings

  Proceedings of the National Academy of Sciences · 2013-10-07 · 40 citations

  articleOpen access1st authorCorresponding

  I studied predictive effects of teachers and schools on test scores in fourth through eighth grade and outcomes later in life such as college attendance and earnings. For example, predict the fraction of a classroom attending college at age 20 given the test score for a different classroom in the same school with the same teacher and given the test score for a classroom in the same school with a different teacher. I would like to have predictive effects that condition on averages over many classrooms, with and without the same teacher. I set up a factor model that, under certain assumptions, makes this feasible. Administrative school district data in combination with tax data were used to calculate estimates and do inference.

  PublisherOA PDFDOI

• Bayesian Aspects of Treatment Choice

  2011-09-29 · 33 citations

  reference-entry1st authorCorresponding

  This article discusses the Bayesian approach to decision theory. It focuses on the case of an individual deciding between treatments. It deals with the role of information that is available about other individuals through a propensity score. It also shows the reason for absence of propensity score in the likelihood function but its appearance in the prior. A prior distribution leads to a closed-form expression for the decision rule. The parametric model plays the role of a prior distribution that can be dominated by the data. The next section examines the role of the propensity score in a random effects model with normal distributions for the outcomes and the random effects. It takes up the extension to the case of treatment selection based on unobservables. The main aim of this article is to estimate an average treatment effect for a particular covariate cell.

  PublisherDOI

• Innovation, knowledge spending and productivity growth in the UK: interim report for NESTA 'Innovation Index’ project

  RePEc: Research Papers in Economics · 2010-02-01 · 17 citations

  preprintOpen access
  PublisherOA PDFDOI

Frequent coauthors

• Guido W. Imbens

  Stanford University

  10 shared

• Michael Rothschild

  7 shared

• Bo E. Honoré

  4 shared

• Sebastien Siegloch

  University College London

  4 shared

• Maximilian Kasy

  4 shared

• Angelo Mele

  4 shared

• Elena Manresa

  New York University

  4 shared

• Elie Tamer

  4 shared

Education

• Ph.D., Economics

  Harvard University

  1984

• B.A., Economics

  University of California, Berkeley

  1979

Awards & honors

• Fellow of the Econometric Society
• Fisher-Schultz Lecture (2001)
• Fellow of the American Academy of Arts and Sciences
• Fellow of the American Association for the Advancement of Sc…
• Member of the National Academy of Sciences