Jerry A. Hausman’s research while affiliated with Massachusetts Institute of Technology and other places

This article surveys the development of nonparametric models and methods for estimation of choice models with nonlinear budget sets. The discussion focuses on the budget set regression, that is, the conditional expectation of a choice variable given the budget set. Utility maximization in a nonparametric model with general heterogeneity reduces the curse of dimensionality in this regression. Empirical results using this regression are different from maximum likelihood and give informative inference. The article also considers the information provided by kink probabilities for nonparametric utility with general heterogeneity. Instrumental variable estimation and the evidence it provides of heterogeneity in preferences are also discussed. Expected final online publication date for the Annual Review of Economics, Volume 15 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

We present a pseudo-panel model and argue that the control variable approach is subject to the many instrument problem, since it uses the predicted value of the endogenous variable. We show how the bias can be analytically characterized. Finally, we demonstrate the problems of split sample cross fitting.

We propose a pragmatic approach to the errors-in-variables and nonlinear panel models. These models are often deemed impossible to estimate in their most general forms. For example, the higher order moments approach to errors-in-variables model fails when there is conditional heteroscedasticity. We propose estimating these models using approximate moments, using a Taylor series approximation applied to Kadane’s (1971) small sigma approach.

This paper proposes a specification test of the mixed logit models, by generalizing Hausman and McFadden (1984) test. We generalize the test even further by considering a model developed by Berry et al. (1995).

This paper shows how to increase the power of Hausman's (1978) specification test as well as the difference test in a large class of models. The idea is to impose the restrictions of the null and the alternative hypotheses when estimating the covariance matrix. If the null hypothesis is true then the proposed test has the same distribution as the existing ones in large samples. If the hypothesis is false then the proposed test statistic is larger with probability approaching one as the sample size increases in several important applications, including testing for endogeneity in the linear model.

Exact consumer’s surplus and deadweight loss are the most widely used welfare and economic efficiency measures. These measures can be computed from demand functions in straightforward ways. Nonparametric estimation can be used to estimate the welfare measures. In doing so, it seems important to account correctly for unobserved heterogeneity, given the high degree of unexplained demand variation often found in applications. This review surveys work on nonparametric welfare analysis, focusing on work that allows for general heterogeneity in demand, such as that of Hausman & Newey (2016). Expected final online publication date for the Annual Review of Economics Volume 9 is August 2, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Paul MacAvoy asked, What is the escape from the situation in which industry growth is held back by the lack of competitive entry brought about by regulatory agencies ostensibly acting in the name of protecting consumers? In this article, we examine MacAvoy's prescient critiques of the failure of antitrust and regulation to produce competitive markets for telecommunications services. We explain how the real competition has come from new products and innovation. Market power in the short run is the incentive that drives investment in research and development, and, in telecommunications, it is a small price to pay for innovative new services. Unfortunately, the experience in telecommunications has been one of regulatory delay of implementing new services and technology to level some hypothetical playing field rather than rewarding innovation with a temporary market advantage. We suggest that technical change enhanced by benign regulatory neglect is the answer to MacAvoy's question. It is the escape from counterproductive regulation to control market power in telecommunications.

This paper considers the finite-sample distribution of the 2SLS estimator and derives bounds on its exact bias in the presence of weak and/or many instruments. We then contrast the behavior of the exact bias expressions and the asymptotic expansions currently popular in the literature, including a consideration of the no-moment problem exhibited by many Nagar-type estimators. After deriving a finite-sample unbiased k-class estimator, we introduce a double-k-class estimator based on Nagar (1962) that dominates k-class estimators (including 2SLS), especially in the cases of weak and/or many instruments. We demonstrate these properties in Monte Carlo simulations showing that our preferred estimators outperform Fuller (1977) estimators in terms of mean bias and MSE.