Focus areas: Foundations of economics and moral philosophy, microeconomics, economic foundations of capital markets, and international finance

Richard Robb teaches courses in microeconomics, economic foundations of capital markets and international finance. In addition, he is CEO of Christofferson, Robb and Company, a New York and London based investment management firm that invests in credit and renewable energy markets in Europe and emerging markets. Prior to arriving at SIPA in 2001, he was the global head of the derivatives and securities subsidiaries of the Dai-Ichi Kangyo Bank, Ltd.

Robb holds a BA from Duke University (1981) and a PhD in economics from the University of Chicago (1985). His publications include "Testing the Mixture of Exponential Hypothesis and Estimating the Mixing Distribution by the Method of Moments," with James Heckman and J. R. Walker in Journal of the American Statistical Association (1990) and "Alternative Methods for Evaluating the Impact of Interventions: An Overview" with James Heckman in Journal of Econometrics (1985).

Research & Publications

March 2017|Applied Stochastic Models in Business and Industry|Richard Robb, Halina Frydman, Andrew Robertson
December 2016|Capitalism and Society|Richard Robb, Martin Sattell
October 2013|Critical Review: A Journal of Politics and Society|Richard Robb
October 2009|Capitalism and Society|Richard Robb
October 1990|Journal of the American Statistical Association |Richard Robb, James Heckman, J. R. Walker

This article presents nonparametric methods for testing the hypothesis that duration data can be represented by a mixture of exponential distributions. Both Bayesian and classical tests are developed. A variety of apparently distinct models can be written in mixture of exponentials form. This raises a fundamental identification problem. A consistent estimator for the number of points of support of a discrete mixture is developed. A consistent method-of-moments estimator for the mixing distribution is derived from the testing criteria and is evaluated in a Monte Carlo study.

October 1985|Journal of Econometrics|Richard Robb, James Heckman

This paper presents methods for estimating the impact of training on earnings when non-random selection characterizes the enrollment of persons into training. We explore the benefits of cross-section, repeated cross-section and longitudinal data for addressing this problem by considering the assumptions required to use a variety of new and conventional estimators given access to various commonly encountered types of data. We investigate the plausibility of assumptions needed to justify econometric procedures when viewed in the light of prototypical decision rules determining enrollment into training. We examine the robustness of the estimators to choice-based sampling and contamination bias.