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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.

Remains accessible for the average reader without advanced knowledge in mathematics
Includes examples of applications to some important problems in econometrics, offering a blue-print for how to apply the techniques in different models
Provides sets of exercises to accompany each chapter



Table of Contents

1. Basic concepts
2. Selections
3. Expectation of random sets
4. Limit theorems for Minkowski sums
5. Estimation and interference.