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Econometric Modeling and Inference

Author

Listed:
  • Jean-Pierre Florens

    (GREMAQ - Groupe de recherche en économie mathématique et quantitative - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique)

  • Vêlayoudom Marimoutou

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Anne Peguin-Feissolle

    (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

Abstract

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.

Suggested Citation

  • Jean-Pierre Florens & Vêlayoudom Marimoutou & Anne Peguin-Feissolle, 2007. "Econometric Modeling and Inference," Post-Print halshs-00390164, HAL.
    • Handle: RePEc:hal:journl:halshs-00390164
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    Citations

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    Cited by:

    1. Frédérique Fève & Jean-Pierre Florens & Leticia Veruete-McKay & Frank Rodriguez & Soterios Steri & Frank Rodriguez, 2012. "Uncertainty and Projections of the Demand for Mail," Chapters, in: Michael A. Crew & Paul R. Kleindorfer (ed.), Multi-Modal Competition and the Future of Mail, chapter 6, Edward Elgar Publishing.
    2. Xiaohong Chen & Andres Santos, 2018. "Overidentification in Regular Models," Econometrica, Econometric Society, vol. 86(5), pages 1771-1817, September.
    3. Julio Vicente Cateia, 2019. "Guinea-Bissau Trade: A Panel Data Analysis," Asian Development Policy Review, Asian Economic and Social Society, vol. 7(4), pages 277-296, December.
    4. Atangana Ondoa, Henri & Tomo, Christian Parfait, 2022. "Déterminants des ménages et accès au crédit dans les tontines au Cameroun [Determinants of households and access to credit in Cameroon]," MPRA Paper 113629, University Library of Munich, Germany, revised Jun 2022.

    More about this item

    Keywords

    Econometrics;

    Statistics

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