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Empirical Process Methods in Econometrics

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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.

Suggested Citation

  • Donald W.K. Andrews, 1993. "Empirical Process Methods in Econometrics," Cowles Foundation Discussion Papers 1059, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:1059
    Note: CFP 887.
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    Cited by:

    1. Chaudhuri, Saraswata & Zivot, Eric, 2011. "A new method of projection-based inference in GMM with weakly identified nuisance parameters," Journal of Econometrics, Elsevier, vol. 164(2), pages 239-251, October.
    2. Nidhaleddine Ben Cheikh & Christophe Rault, 2016. "Recent estimates of exchange rate pass-through to import prices in the euro area," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 152(1), pages 69-105, February.
    3. Arthur Lewbel & Oliver Linton, 2003. "Nonparametric estimation of homothetic and homothetically separable functions," CeMMAP working papers 14/03, Institute for Fiscal Studies.
    4. David Powell, 2013. "A New Framework for Estimation of Quantile Treatment Effects Nonseparable Disturbance in the Presence of Covariates," Working Papers WR-824-1, RAND Corporation.
    5. 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.
    6. F Bravo, 2008. "Effcient M-estimators with auxiliary information," Discussion Papers 08/26, Department of Economics, University of York.
    7. 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.
    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. David Powell, 2020. "Quantile Treatment Effects in the Presence of Covariates," The Review of Economics and Statistics, MIT Press, vol. 102(5), pages 994-1005, December.

    More about this item

    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other

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