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

Brent Kreider () and John Pepper

Staff General Research Papers Archive from Iowa State University, Department of Economics

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.

Date: 2011-02-02
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (9)

Published in Journal of Business & Economic Statistics, January 2011, vol. 29 no. 1, pp. 49-60

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Related works:
Journal Article: Identification of Expected Outcomes in a Data Error Mixing Model With Multiplicative Mean Independence (2011)
Journal Article: Identification of Expected Outcomes in a Data Error Mixing Model With Multiplicative Mean Independence (2011)
Working Paper: Identification of Expected Outcomes in a Data Error Mixing Model With Multiplicative Mean Independence (2011)
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