Professor Jerry Hausman

From its inception, demand estimation has faced the problem of “many prices.”

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.

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

We propose a new estimator for the dynamic panel model, which solves the failure of strict exogeneity by calculating the bias in the first-order conditions as a function of the autoregressive parameter and solving the resulting equation.

Individual heterogeneity is an important source of variation in demand. Allowing for general heterogeneity is needed for correct welfare comparisons. We consider general heterogeneous demand where preferences and linear budget sets are statistically independent.

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 consider general heterogenous demand where preferences and linear budget sets are statistically independent.

We consider general heterogenous demand where preferences and linear budget sets are statistically independent.

This paper gives a relatively simple, well behaved solution to the problem of many instruments in heteroskedastic data.

In this paper, we analyze properties of the Continuous Updating Estimator (CUE) proposed by Hansen et al. (1996), which has been suggested as a solution to the finite sample bias problems of the two-step GMM estimator.

In this paper, we introduce a new flexible mixed model for multinomial discrete choice where the key individual- and alternative-specific parameters of interest are allowed to follow an assumption-free nonparametric density specification, while other alternative-specific coefficients are assumed to be drawn from a multivariate Normal distribution, which eliminates the independence of irrelevant alternatives assumption at the individual level.

We give corrected standard errors, an extension of Bekker (1994) to nonnormal disturbances, that adjust for many instruments.

In this paper we consider consumer benefits from increased competition in a differentiated product setting: the spread of nontraditional retail outlets.

Current methods of estimating the random coefficients logit model employ simulations of the distribution of the taste parameters through pseudo-random sequences.

In this paper we modify CUE to solve the no moments/large dispersion problem.

This paper is a revised version of CWP01/06

Using many valid instrumental variables has the potential to improve efficiency but makes the usual inference procedures inaccurate.

In this paper we consider consumer benefits from increased competition in a differentiated product setting: the spread of non-traditional retail outlets.