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Testing the normality assumption in the sample selection model with an application to travel demand

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  • Klaauw, B. van der
  • Koning, R.H.

    (Groningen University)

Abstract

In this paper we introduce a test for the normality assumption in the sample selection model.The test is based on a generalization of a semi-nonparametric maximum likelihood method.In this estimation method,the distribution of the error erms is approximated by a Hermite series,with normality as a special case.Because all parameters of the model are estimated both under normality and in the more general specification,we can est for normality using the likeli- hood ratio approach.This est has reasonable power as is shown by a simulation study.Finally,we apply the generalized semi-nonparametric maximum likeli- hood estimation method and the normality est o a model of car ownership and car use.The assumption of normal distributed error erms is rejected and we provide estimates of the sample selection model that are consisten .

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File URL: http://irs.ub.rug.nl/ppn/240570626
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Paper provided by University of Groningen, Research Institute SOM (Systems, Organisations and Management) in its series Research Report with number 00F37.

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Date of creation: 2000
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Handle: RePEc:dgr:rugsom:00f37

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  1. repec:mea:ivswpa:430 is not listed on IDEAS
  2. Melenberg, B. & Van Soest, A., 1993. "Semi-Parametric Estimation on the Sample Selection Model," Papers 9334, Tilburg - Center for Economic Research.
  3. Cameron, A Colin & Johansson, Per, 1997. "Count Data Regression Using Series Expansions: With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 203-23, May-June.
  4. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-90, March.
  5. Charles F. Manski, 1989. "Anatomy of the Selection Problem," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 343-360.
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