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Daniel McFadden
Researcher at University of California, Berkeley
Publications - 248
Citations - 63965
Daniel McFadden is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Medicare Part D & Estimator. The author has an hindex of 74, co-authored 243 publications receiving 60638 citations. Previous affiliations of Daniel McFadden include Cambridge Systematics & University of California.
Papers
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A theory of the perturbed consumer with general budgets
Daniel McFadden,Mogens Fosgerau +1 more
TL;DR: In this article, the authors consider demand systems for utility-maximizing consumers with general budget constraints whose utilities are perturbed by additive linear shifts in marginal utilities, and define demand generating functions (DGF) whose subgradients with respect to these perturbations are convex hulls of the utility maximizing demands.
Journal Article
Is vehicle depreciation a component of marginal travel cost?: a literature review and empirical analysis
TL;DR: The authors examined empirically whether depreciation is related to households' decisions of how much to drive and found that, relative to fuel costs, depreciation has a small effect on the amount that households drive.
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Estimation of some partially specified nonlinear models
Chunrong Ai,Daniel McFadden +1 more
TL;DR: In this paper, the authors present a procedure for analyzing a partially specified nonlinear regression model in which the nuisance parameter is an unrestricted function of a subset of regressors and the model parameters are estimated by applying Robinson's (1988a) procedure and the estimator is show to be √N-consistent and asymptotically normal.
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A simple remark on the second best pareto optimality of market equilibria
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Advances in Computation, Statistical Methods, and Testing of Discrete Choice Models
TL;DR: A brief overview of recent developments in computation, estimation, and statistical testing of choice models, with marketing applications, is given in this article, which includes statistical models for discrete panel data with heterogeneous decision makers, simulation methods for estimation of high-dimension multinomial probit models, specification tests for model structure and for brand and purchase clustering, and innovations in numerical analysis for estimation and forecasting.