Random Sets in Econometrics by Ilya Molchanov download in iPad, ePub, pdf
This is an intermediate level, Ph. Francesca Molinari is the H.
Perform a search for a similarly titled item that would be available. This allows to link your profile to this item. Their book provides the foundation for much more to come.
We have no references for this item. Partial identification analysis has become important in empirical economics, largely motivated by revealed preference ideas.
Molchanov and Molinari define the basics of the theory and illustrate the mathematical concepts by their application in the analysis of econometric models. She authored numerous papers in this area, including empirical ones on estimation of risk preferences using property insurance data.
We will consider numerous applications from the literature, including static and dynamic regression models, heterogeneous parameters models e. Random sets provide a useful and important way of approaching these problems. 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. It also allows you to accept potential citations to this item that we are uncertain about. They are particularly helpful in obtaining sharp identified sets, e.
Molchanov and Molinari, a brilliant collaboration of mathematician and econometrician, present the fundamentals meticulously and describe the fruitful applications to date. The following list points to the class discussion notes for Econometric Analysis of Panel Data. She is a specialist in econometric theory, with emphasis on partial identification.
The book includes sets of exercises to accompany each chapter as well as examples to help readers apply the theory effectively. His research and publications focus on probability theory, spatial statistics, and mathematical finance, with the main emphasis on stochastic geometry and the theory of random sets. Topics to be studied include specification, estimation, and inference in the context of models that include individual firm, person, etc. 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. This book is timely and useful in providing an introduction to this new area that is clear and gives the necessary mathematical background for a good understanding of and research on these methods.
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