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Random Sets in Econometrics
Contributor(s): Molchanov, Ilya (Author), Molinari, Francesca (Author)

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 ISBN: 110754873X     ISBN-13: 9781107548732
Publisher: Cambridge University Press
OUR PRICE: $35.14  

Binding Type: Paperback - See All Available Formats & Editions
Published: April 2018
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Click for more in this series: Econometric Society Monographs (Paperback)
Additional Information
BISAC Categories:
- Business & Economics | Econometrics
- Business & Economics | Economics - General
Dewey: 330.015
LCCN: 2017055335
Series: Econometric Society Monographs (Paperback)
Physical Information: 0.56" H x 6.51" W x 8.96" L (0.63 lbs) 194 pages
Features: Bibliography, Index, Price on Product
 
Descriptions, Reviews, Etc.
Publisher Description:
Random set theory is a fascinating branch of mathematics that amalgamates techniques from topology, convex geometry, and probability theory. Social scientists routinely conduct empirical work with data and modelling assumptions that reveal a set to which the parameter of interest belongs, but not its exact value. Random set theory provides a coherent mathematical framework to conduct identification analysis and statistical inference in this setting and has become a fundamental tool in econometrics and finance. This is the first book dedicated to the use of the theory in econometrics, written to be accessible for readers without a background in pure mathematics. Molchanov and Molinari define the basics of the theory and illustrate the mathematical concepts by their application in the analysis of econometric models. The book includes sets of exercises to accompany each chapter as well as examples to help readers apply the theory effectively.

Contributor Bio(s): Molinari, Francesca: - Francesca Molinari is the H. T. Warshow and Robert Irving Warshow Professor of Economics at Cornell University, New York. She is a specialist in econometric theory, with emphasis on partial identification. She authored numerous papers in this area, including empirical ones on estimation of risk preferences using property insurance data.
 
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