Econometric Modeling and Inference by Jean Pierre Florens

Econometric Modeling & Inference by Jean Pierre Florens

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Presents 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. The choice of GMM is explained by its relevance in structural econometrics and its preeminent position in econometrics overall. Split into four parts, Part I explains general methods. Part II studies statistical models that are best suited for microeconomic data. Part III deals with dynamic models that are designed for macroeconomic and financial applications. In Part IV the authors synthesize a set of problems that are specific to statistical methods in structural econometrics, namely identification and over-identification, simultaneity, and unobservability. Many theoretical examples illustrate the discussion and can be treated as application exercises. Nobel Laureate James A. Heckman offers a foreword to the work.

Review

“…an updated and well-balanced bird’s-eye view of the basic econometric concepts and analyses with informative references on the respective topics for the benefit of readers’ further study, covering the traditional simultaneous-equation approach as well as the recent parametric and semiparametric time-series analyses.”
Yuzo Hosoya, Mathematical Reviews

“… May make a great contribution to teaching the next generation of theoretical econometricians. For the statistician comfortable with formal mathematics, Econometric Modeling and Inference provides an excellent, low-cost opportunity to catch up with what the econometrics subfield has been doing.”
Richard Startz, University of Washington for Journal of the American Statistical Association

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.

Vêlayoudom Marimoutou is Professor of Economics at the University of Aix-Marseille 2 and a member of GREQAM. His research fields include: time series analysis, non-stationary processes, long range dependence, and applied econometrics of exchange rates, finance, macroeconometrics, convergence, and international trade. His articles have appeared in publications such as the Journal of International Money and Finance, Oxford Bulletin of Economics and Statistics, and the Journal of Applied Probability.

Anne Peguin-Feissolle is Research Director of the National Center of Scientific Research (CNRS) and a member of the GREQAM. She conducts research on econometric modelling, especially nonlinear econometrics, applications to macroeconomics, finance, spatial economics, artificial neural network modelling, and long memory problems. Professor Peguin-Feissolle’s published research has appeared in Economics Letters, Economic Modelling, European Economic Review, Applied Economics, and the Annales d’Economie et de Statistique, among other publications.

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