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Censored regression analysis in large samples with many zero observations

Author

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  • Franses, Ph.H.B.F.
  • Slagter, E.
  • Cramer, J.S.

Abstract

With the advent of improved data collection techniques, the applied econometrician can nowadays have access to very large data bases. Sometimes, however, these can have fairly low informational content. For example, a typical response rate in direct mailings is below 1%. Given the small fraction of respondents, one could be tempted to omit the larger part of the nonrespondents from the analysis. If so, one should adapt the statistical analysis to this new situation. We put forward such an adaptation for the censored regression model. This model is often used in marketing research, for example, to analyze the amount of money spent on new products offered in a direct mailing campaign. We discuss how the likelihood function should be modified to obtain proper maximum likelihood [ML] estimates. Our empirical illustration concerns a data set of about 300000 observations. We show that our modified ML method yields the appropriate estimates, and that the loss of efficiency is not large.

Suggested Citation

  • Franses, Ph.H.B.F. & Slagter, E. & Cramer, J.S., 1999. "Censored regression analysis in large samples with many zero observations," Econometric Institute Research Papers EI 9939-A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1608
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    Cited by:

    1. Sabine Knapp & Philip Hans Franses, 2009. "Comprehensive Review of the Maritime Safety Regimes: Present Status and Recommendations for Improvements," Transport Reviews, Taylor & Francis Journals, vol. 30(2), pages 241-270, April.
    2. Fok, Dennis & Franses, Philip Hans, 2002. "Ordered logit analysis for selectively sampled data," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 477-497, September.
    3. Barry L. Bayus, 2013. "Crowdsourcing New Product Ideas over Time: An Analysis of the Dell IdeaStorm Community," Management Science, INFORMS, vol. 59(1), pages 226-244, June.
    4. Bashirzadeh, Yashar & Mai, Robert & Faure, Corinne, 2022. "How rich is too rich? Visual design elements in digital marketing communications," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 58-76.
    5. Fan, Di & Yeung, Andy C.L. & Yiu, Daphne W. & Lo, Chris K.Y., 2022. "Safety regulation enforcement and production safety: The role of penalties and voluntary safety management systems," International Journal of Production Economics, Elsevier, vol. 248(C).
    6. J. S. Cramer, 2004. "Scoring bank loans that may go wrong: a case study," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(3), pages 365-380, August.
    7. Fan, Di & Lo, Chris K.Y. & Zhou, Yi, 2021. "Sustainability risk in supply bases: The role of complexity and coupling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).

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