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Moment Based Inference with Stratified Data

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  • Gautam Tripathi

    (University of Connecticut)

Abstract

Many datasets used by economists and other social scientists are collected by stratified sampling. The sampling scheme used to collect the data induces a probability distribution on the observed sample that differs from the target or underlying distribution for which inference is to be made. If this effect is not taken into account, subsequent statistical inference can be seriously biased. This paper shows how to do efficient semiparametric inference in moment restriction models when data from the target population is collected by three widely used sampling schemes: variable probability sampling, multinomial sampling, and standard stratified sampling.

Suggested Citation

  • Gautam Tripathi, 2005. "Moment Based Inference with Stratified Data," Working papers 2005-38, University of Connecticut, Department of Economics, revised Jan 2007.
    • Handle: RePEc:uct:uconnp:2005-38
    Note: I thank the co-editors and two anonymous referees for comments that greatly improved this paper. I also thank Paul Devereux and seminar participants at several universities for helpful suggestions and conversations. Financial support for this project from NSF grant SES-0214081 is gratefully acknowledged.
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    References listed on IDEAS

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    1. Wooldridge, Jeffrey M., 2001. "Asymptotic Properties Of Weighted M-Estimators For Standard Stratified Samples," Econometric Theory, Cambridge University Press, vol. 17(2), pages 451-470, April.
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    Cited by:

    1. Yuichi Kitamura, 2007. "Nonparametric Likelihood: Efficiency And Robustness," The Japanese Economic Review, Japanese Economic Association, vol. 58(1), pages 26-46, March.
    2. Esmeralda Ramalho & Joaquim Ramalho, 2006. "Bias-Corrected Moment-Based Estimators for Parametric Models Under Endogenous Stratified Sampling," Econometric Reviews, Taylor & Francis Journals, vol. 25(4), pages 475-496.
    3. Esmeralda A. Ramalho & Joaquim J. S. Ramalho, 2006. "Two‐Step Empirical Likelihood Estimation Under Stratified Sampling When Aggregate Information Is Available," Manchester School, University of Manchester, vol. 74(5), pages 577-592, September.
    4. Kyungchul Song, 2009. "Efficient Estimation of Average Treatment Effects under Treatment-Based Sampling," PIER Working Paper Archive 09-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    5. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(3), pages 1053-1079.
    6. Yuichi Kitamura, 2006. "Empirical Likelihood Methods in Econometrics: Theory and Practice," CIRJE F-Series CIRJE-F-430, CIRJE, Faculty of Economics, University of Tokyo.
    7. Tripathi, Gautam, 2011. "Generalized method of moments (GMM) based inference with stratified samples when the aggregate shares are known," Journal of Econometrics, Elsevier, vol. 165(2), pages 258-265.

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    More about this item

    Keywords

    Empirical likelihood; Moment conditions; Stratified sampling.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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