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Nested logit analysis of missing response observations


  • Byung-Joo Lee
  • Lawrence Marsh


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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    5. Jan Fagerberg & Bart Verspagen, 1996. "Heading for Divergence? Regional Growth in Europe Reconsidered," Journal of Common Market Studies, Wiley Blackwell, vol. 34(3), pages 431-448, September.
    6. Kaldor, Nicholas, 1970. "The Case for Regional Policies," Scottish Journal of Political Economy, Scottish Economic Society, vol. 17(3), pages 337-348, November.
    7. Helmut Hofer & Andreas Worgotter, 1997. "Regional Per Capita Income Convergence in Austria," Regional Studies, Taylor & Francis Journals, vol. 31(1), pages 1-12.
    8. Gudgin, Graham, 1995. "Regional Problems and Policy in the UK," Oxford Review of Economic Policy, Oxford University Press, vol. 11(2), pages 18-63, Summer.
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