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Using Weights to Adjust for Sample Selection When Auxiliary Information is Available

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  • Aviv Nevo

Abstract

In this paper I analyze GMM estimation when the sample is not a random draw from the population of interest. I exploit auxiliary information, in the form of moments from the population of interest, in order to compute weights that are proportional to the inverse probability of selection. The essential idea is to construct weights, for each observation in the primary data, such that the moments of the weighted data are set equal to the additional moments. The estimator is applied to the Dutch Transportation Panel, in which refreshment draws were taken from the population of interest in order to deal with heavy attrition of the original panel. I show how these additional samples can be used to adjust for sample selection.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0275.

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Date of creation: Nov 2001
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Publication status: published as Nevo, Aviv. "Using Weights To Adjust For Sample Selection When Auxiliary Information Is Available," Journal of Business and Economic Statistics, 2003, v21(1,Jan), 43-52.
Handle: RePEc:nbr:nberte:0275

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Cited by:
  1. Bryan S. Graham & Cristine Campos de Xavier Pinto & Daniel Egel, 2008. "Inverse Probability Tilting for Moment Condition Models with Missing Data," NBER Working Papers 13981, National Bureau of Economic Research, Inc.
  2. Lechner, Michael, 2004. "Sequential Matching Estimation of Dynamic Causal Models," IZA Discussion Papers 1042, Institute for the Study of Labor (IZA).
  3. Prokhorov, Artem & Schmidt, Peter, 2009. "GMM redundancy results for general missing data problems," Journal of Econometrics, Elsevier, Elsevier, vol. 151(1), pages 47-55, July.
  4. Marcel Das & Vera Toepoel & Arthur van Soest, 2011. "Nonparametric Tests of Panel Conditioning and Attrition Bias in Panel Surveys," Sociological Methods & Research, , , vol. 40(1), pages 32-56, February.
  5. Paul J. Devereux & Gautam Tripathi, 2005. "Optimally Combining Censored and Uncensored Datasets," Working papers, University of Connecticut, Department of Economics 2005-10, University of Connecticut, Department of Economics, revised Oct 2007.
  6. Bhattacharya, Debopam, 2008. "Inference in panel data models under attrition caused by unobservables," Journal of Econometrics, Elsevier, Elsevier, vol. 144(2), pages 430-446, June.
  7. Hindsley, Paul & Landry, Craig E. & Gentner, Brad, 2011. "Addressing onsite sampling in recreation site choice models," Journal of Environmental Economics and Management, Elsevier, vol. 62(1), pages 95-110, July.
  8. Emre Ekinci, 2009. "Dealing with Attrition When Refreshment Samples are Available: An Application to the Turkish Household Labor Force Survey," 2009 Meeting Papers, Society for Economic Dynamics 353, Society for Economic Dynamics.

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