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Empirical process methods in econometrics

In: Handbook of Econometrics

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  • Andrews, Donald W.K.

Abstract

This paper provides an introduction to the use of empirical process methods in econometrics. These methods can be used to establish the large sample properties of econometric estimators and test statistics. In the first part of the paper, key terminology and results are introduced and discussed heuristically. Applications in the econometrics literature are briefly reviewed. A select set of three classes of applications is discussed in more detail.The second part of the paper shows how one can verify a key property called stochastic equicontinuity. The paper takes several stochastic equicontinuity results from the probability literature, which rely on entropy conditions of one sort or another, and provides primitive sufficient conditions under which the entropy conditions hold. This yields stochastic equicontinuity results that are readily applicable in a variety of contexts. Examples are provided.

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This chapter was published in:

  • R. F. Engle & D. McFadden (ed.), 1986. "Handbook of Econometrics," Handbook of Econometrics, Elsevier, edition 1, volume 4, number 4, January.
    This item is provided by Elsevier in its series Handbook of Econometrics with number 4-37.

    Handle: RePEc:eee:ecochp:4-37

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    Web page: http://www.elsevier.com/wps/find/bookseriesdescription.cws_home/BS_HE/description

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    Cited by:
    1. Ivana Komunjer, 2007. "Asymmetric power distribution: Theory and applications to risk measurement," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 891-921.
    2. Arthur Lewbel & Oliver Linton, 2003. "Nonparametric Estimation of Homothetic and Homothetically Separable Functions," STICERD - Econometrics Paper Series /2003/461, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    3. Ramdan Dridi, 2000. "Simulated Asymptotic Least Squares Theory," STICERD - Econometrics Paper Series /2000/396, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    4. Steven Berry & Oliver Linton & Ariel Pakes, 2002. "Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems," Cowles Foundation Discussion Papers 1372, Cowles Foundation for Research in Economics, Yale University.
    5. Donald W.K. Andrews & Werner Ploberger, 1993. "Admissibility of the Likelihood Ratio Test When a Nuisance Parameter Is Present OnlyUnder the Alternative," Cowles Foundation Discussion Papers 1058, Cowles Foundation for Research in Economics, Yale University.
    6. Xiaohong Chen & Oliver Linton & Ingred Van Keilegom, 2002. "Estimation of semiparametric models when the criterion function is not smooth," CeMMAP working papers CWP02/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. F Bravo, 2008. "Effcient M-estimators with auxiliary information," Discussion Papers 08/26, Department of Economics, University of York.
    8. Adonis Yatchew & Len Bos, 1997. "Nonparametric Least Squares Regression and Testing in Economic Models," Working Papers yatchew-99-01, University of Toronto, Department of Economics.
    9. Ramdan Dridi & Eric Renault, 2000. "Semi-Parametric Indirect Inference," STICERD - Econometrics Paper Series /2000/392, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    10. Saraswata Chaudhuri & Eric Zivot, 2008. "A new method of projection-based inference in GMM with weakly identified nuisance parameters," Working Papers UWEC-2008-26, University of Washington, Department of Economics.
    11. Woocheol Kim & Oliver Linton, 2003. "A Local Instrumental Variable Estimation Method for Generalized Additive Volatility Models," STICERD - Econometrics Paper Series /2003/456, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.

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