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Moment Based Inference with Stratified Data

  • Gautam Tripathi

    (University of Connecticut)

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.

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File URL: http://web2.uconn.edu/economics/working/2005-38r.pdf
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File URL: http://web2.uconn.edu/economics/working/2005-38.pdf
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Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2005-38.

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Length: 28 pages
Date of creation: Sep 2005
Date of revision: 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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Web page: http://www.econ.uconn.edu/

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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(02), pages 451-470, April.
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