Semiparametric and nonparametric methods in econometrics
General Material Designation
[Book]
First Statement of Responsibility
Joel L. Horowitz
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
New York :
Name of Publisher, Distributor, etc.
Springer,
Date of Publication, Distribution, etc.
c2009
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (x, 271 p.) :
Other Physical Details
ill
SERIES
Series Title
Springer series in statistics
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index
CONTENTS NOTE
Text of Note
Preface; Contents; 1 Introduction; 2 Single-Index Models; 3 Nonparametric Additive Models and Semiparametric Partially Linear Models; 4 Binary-Response Models; 5 Statistical Inverse Problems; 6 Transformation Models; Appendix: Nonparametric Density Estimationand Nonparametric Regression; References; Index
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SUMMARY OR ABSTRACT
Text of Note
Standard methods for estimating empirical models in economics and many other fields rely on strong assumptions about functional forms and the distributions of unobserved random variables. Often, it is assumed that functions of interest are linear or that unobserved random variables are normally distributed. Such assumptions simplify estimation and statistical inference but are rarely justified by economic theory or other a priori considerations. Inference based on convenient but incorrect assumptions about functional forms and distributions can be highly misleading. Nonparametric and semiparam
OTHER EDITION IN ANOTHER MEDIUM
Title
Semiparametric and nonparametric methods in econometrics.