Proceedings of the National Academy of Sciences of the United States of America

About the PNAS Member Editor
Name Chamberlain, Gary E.
Location Harvard University
Primary Field Economic Sciences
 Election Citation
Among many notable contributions to econometric methods, those applicable to the study of panel or longitudinal data are especially noteworthy. Chamberlain and Griliches made clear for the first time, the simultaneous-equations nature of the problem of the correlation between observed explanatory variables and the unobserved individual-specific components of the disturbance.
 Research Interests
Gary Chamberlain has worked on the use of sibling data to measure economic returns to education in the presence of unmeasured ability variables. He has developed methods for using longitudinal data to deal with omitted variable bias. He has applied quantile regression methods to provide richer descriptions of the structure of wages, and developed Bayesian methods to provide predictive distributions based on longitudinal earnings data. He has worked on asymptotic efficiency in estimation with conditional moment restrictions, in semiparametric models with censoring, and in panel data models with correlated random effects. He has applied finite-sample decision theory to instrumental variable models that address selection bias, and to panel data models. He has used Bayesian decision theory to develop rules for individual treatment choice that condition on characteristics of the individual and on a sample of other individuals with data on characteristics, treatments, and outcomes. He is currently interested in methods motivated by education data sets, including the measurement of teacher effects.

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