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Econometric modeling and inference / Jean-Pierre Florens, Vêlayoudom Marimoutou, Anne Péguin-Feissolle ; translated by, Josef Perktold and Marine Carrasco ; foreword by James J. Heckman.

By: Contributor(s): Material type: TextTextLanguage: English Original language: French Series: Themes in modern econometricsPublisher: Cambridge : Cambridge University Press, 2007Description: 1 online resource (xxi, 496 pages) : digital, PDF file(s)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780511805592 (ebook)
Other title:
  • Econometric Modeling & Inference
Uniform titles:
  • Économétrie. English
Subject(s): Additional physical formats: Print version: : No titleDDC classification:
  • 330.01/5195 22
LOC classification:
  • HB141 .F5913 2007
Online resources: Summary: 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.
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Title from publisher's bibliographic system (viewed on 05 Oct 2015).

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.

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