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Nested logit analysis of missing response observations

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  • Byung-Joo Lee
  • Lawrence Marsh

Abstract

A new estimation technique is proposed to deal with missing response variables in the context of a nested multinomial logit model. Survey data often have a significant number of incomplete or missing responses. If such data are systematically missing (i.e. not missing at random) and if such observations are deleted from the analysis, biased sample selection results. The new method is applied to the empirical analysis of determining job loss status.

Suggested Citation

  • Byung-Joo Lee & Lawrence Marsh, 1998. "Nested logit analysis of missing response observations," Applied Economics Letters, Taylor & Francis Journals, vol. 5(12), pages 751-755.
    • Handle: RePEc:taf:apeclt:v:5:y:1998:i:12:p:751-755
    DOI: 10.1080/135048598353943
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    References listed on IDEAS

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    1. Lien, Donald & Rearden, David, 1990. "Missing measurements in discrete response models," Economics Letters, Elsevier, vol. 32(3), pages 231-235, March.
    2. Guttman, Irwin & Menzefricke, Ulrich, 1983. "Bayesian Inference in Multivariate Regression with Missing Observations on the Response Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(3), pages 239-248, July.
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