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Identification of Expected Outcomes in a Data Error Mixing Model with Multiplicative Mean Independence

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Author Info
Kreider, Brent
Pepper, John V.

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Abstract

We consider the problem of identifying a mean outcome in corrupt sampling where the observed outcome is a mixture of the distribution of interest and some other distribution. We make two contributions to this literature. First, the statistical independence assumption maintained under contaminated sampling is relaxed to the weaker assumption that the outcome is mean independent of the mixing process. We then generalize this restriction to allow the two conditional means to differ by a known or bounded factor of proportionality. Second, in the special case of a binary outcome, we consider the possibility that draws from the alternative distribution are known to be erroneous, as might be the case in a mixture model of response error. We illustrate how these assumptions can be used to inform researchers about the population's use of illicit drugs in the presence of nonrandom reporting errors. In this application, we find that a response error model with multiplicative mean independence is easy to motivate and can have substantial identifying power.

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Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number 12496.

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Date of creation: 02 Feb 2006
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Publication status: Forthcoming in Journal of Business and Economic Statistics
Handle: RePEc:isu:genres:12496

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Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070
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Related research
Keywords: measurement error; identification; contaminated sampling; corrupt sampling; nonparametric bounds;

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C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843 Elsevier. [Downloadable!] (restricted)
  2. E. Tamer & V. Chernozhukov & H. Hong, 2004. "Parameter Set Inference in a Class of Econometric Models," Econometric Society 2004 North American Winter Meetings 382, Econometric Society.
  3. Molinari, Francesca, 2008. "Partial identification of probability distributions with misclassified data," Journal of Econometrics, Elsevier, vol. 144(1), pages 81-117, May. [Downloadable!] (restricted)
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  4. Barron, John M & Berger, Mark C & Black, Dan A, 1997. "Employer Search, Training, and Vacancy Duration," Economic Inquiry, Oxford University Press, vol. 35(1), pages 167-92, January.
  5. Frazis, Harley & Loewenstein, Mark A., 2003. "Estimating linear regressions with mismeasured, possibly endogenous, binary explanatory variables," Journal of Econometrics, Elsevier, vol. 117(1), pages 151-178, November. [Downloadable!] (restricted)
  6. Bound, John & Burkhauser, Richard V., 1999. "Economic analysis of transfer programs targeted on people with disabilities," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 51, pages 3417-3528 Elsevier. [Downloadable!] (restricted)
  7. Kreider, Brent & Pepper, John V., 2003. "Inferring Disability Status from Corrupt Data," Staff General Research Papers 10228, Iowa State University, Department of Economics.
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  8. Mark C. Berger & Dan A. Black & Frank A. Scott, 1998. "How Well Do We Measure Employer-Provided Health Insurance Coverage?," Contemporary Economic Policy, Western Economic Association International, vol. 16(3), pages 356-367, 07. [Downloadable!] (restricted)
  9. Bound, John, 1991. "The Health and Earnings of Rejected Disability Insurance Applicants: Reply," American Economic Review, American Economic Association, vol. 81(5), pages 1427-34, December. [Downloadable!] (restricted)
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