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The DOGEV Model

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  • Tim R.L. Fry
  • Mark N. Harris

Abstract

At present, there appears to be no qualitative dependent model that can simultaneously account for data sets in which the variable of interest is potentially ordered but also has strong heterogeneity of the observed outcomes. This heterogeneity of particular outcomes, inherently attracts individuals to them, in addition to that determined by the individual's observed characteristics. An example of such unobserved heterogeneity would be brand-loyalty (or "captivity") in a model of consumer choice. Such heterogeneity of the outcomes, may well result in a pronounced multi-modal distribution of the variable of interest. This paper introduces the Dogit Ordered Generalized Extreme Value (DOGEV) model, which does account for both ordering and captivity (and/or multiple modes) in the data. In the spirit of Manski (1977), the DOGEV model combines a model for choice set generation with the Ordered Generalized Extreme Value model. We illustrate the model using three different empirical examples: a model of employment contract types; an inflationary expectations data set and; a survey of students' evaluations of teaching. These three examples are chosen as they represent different values that the additional ancillary parameters are likely to take in practice.

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Bibliographic Info

Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 7/02.

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Length: 35 pages
Date of creation: Aug 2002
Date of revision:
Handle: RePEc:msh:ebswps:2002-7

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Related research

Keywords: Generalized extreme value; Choice set generation; Ordinal data;

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References

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  1. Kannan, P. K. & Yim, Chi Kin (Bennett), 2001. "An investigation of the impact of promotions on across-submarket competition," Journal of Business Research, Elsevier, vol. 53(3), pages 137-149, September.
  2. Swait, Joffre, 2001. "Choice set generation within the generalized extreme value family of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 643-666, August.
  3. Marc J. I. Gaudry, 1980. "Dogit and Logit Models of Travel Mode Choice in Montreal," Canadian Journal of Economics, Canadian Economics Association, vol. 13(2), pages 268-79, May.
  4. Tse, Y K, 1987. "A Diagnostic Test for the Multinomial Logit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(2), pages 283-86, April.
  5. Fry, T R L, et al, 1993. "Economic Motivations for Limited Dependent and Qualitative Variable Models," The Economic Record, The Economic Society of Australia, vol. 69(205), pages 193-205, June.
  6. Koppelman, Frank S. & Wen, Chieh-Hua, 2000. "The paired combinatorial logit model: properties, estimation and application," Transportation Research Part B: Methodological, Elsevier, vol. 34(2), pages 75-89, February.
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Cited by:
  1. Ugo Colombino & Rolf Aaberge & Tom Wennemo, 2005. "Evaluating Alternative Representations of the Choice Sets in Models of Labour Supply," Econometrics 0510001, EconWPA.
  2. Eyob Fissuh & Mark Harris, 2004. "Determinants of Poverty in Eritrea: A Household level Analysis," Econometric Society 2004 Australasian Meetings 364, Econometric Society.

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