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Synopsis
The aim of this book is to present the main statistical tools of econometrics. It covers almost all modern econometric methodology and unifies the approach by using a small number of estimation techniques, many from generalized method of moments (GMM) estimation. The work is in four parts: Part I sets forth statistical methods, Part II covers regression models, Part III investigates dynamic models, and Part IV synthesizes a set of problems that are specific models in structural econometrics, namely identification and overidentification, simultaneity, and unobservability. Many theoretical examples illustrate the discussion and can be treated as application exercises.
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Book Description
The goal of this book is to present the main statistical tools of econometrics, focusing specifically on modern econometric methodology. The authors unify the approach by using a small number of estimation techniques, mainly generalized method of moments (GMM) estimation and kernel smoothing.
About the Author
Jean-Pierre Florens is Professor of Mathematics at the University of Toulouse I, where he holds the Chair in Statistics and Econometrics, and a senior member of the Institut Universitaire de France. He is also a member of the IDEI and GREMAQ research groups. Professor Florens' research interests include: statistics and econometrics methods, applied econometrics, and applied statistics. He is coauthor of Elements of Bayesian Statistics with Michel Mouchart and Jean-Marie Rolin (1990). The editor or co-editor of several econometrics and statistics books, he has also published numerous articles in the major econometric reviews, such as Econometrica, Journal of Econometrics, and Econometric Theory.
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- PublisherCambridge University Press
- Publication date2007
- ISBN 10 052170006X
- ISBN 13 9780521700061
- BindingPaperback
- LanguageEnglish
- Number of pages518
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