Structural Analysis of Discrete Data with Econometric Applications
The thirteen papers in Structural Analysis of Discrete Data are previously unpublished major research contributions solicited by the editors. They have been specifically prepared to fulfill the two-fold purpose of the volume, first to provide the econometrics student with an overview of the present extent of the subject and to delineate the boundaries of current research, both in terms of methodology and applications. "Coordinated publication of important findings" should, as the editors state, "lower the cost of entry into the field and speed dissemination of recent research into the graduate econometrics classroom."
A second purpose of the volume is to communicate results largely reported in the econometrics literature to a wider community of researchers to whom they are directly relevant, including applied econometricians, statisticians in the area of discrete multivariate analysis, specialists in biometrics, psychometrics, and sociometrics, and analysts in various applied fields such as finance, marketing, and transportation.
The papers are grouped into four sections: Statistical Analysis of Discrete Probability Models, with papers by the editors and by Steven Cosslett; Dynamic Discrete Probability Models, consisting of two contributions by James Heckman; Structural Discrete Probability Models Derived from Theories of Choice, with papers by Daniel McFadden, Gregory Fischer and Daniel Nagin, Steven Lerman and Charles Manski, and Moshe Ben-Akiva and Thawat Watanatada; and Simultaneous Systems Models with Discrete Endogenous Variables, with contributions by Lung-Fei Lee, Jerry Hausman and David Wise, Dale Poirier, Peter Schmidt, and Robert Avery.
Among the applications treated are income maintenance experiments, physician behavior, consumer credit, and intra-urban location and transportation.
Specifications of Structural Analysis of Discrete Data with Econometric Applications
|Publisher||The MIT Press|
|Number Of Pages||504|
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