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Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates

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  • Papke, Leslie E
  • Wooldridge, Jeffrey M

Abstract

We develop attractive functional forms and simple quasi-likelihood estimation methods for regression models with a fractional dependent variable. Compared with log-odds type procedures, there is no difficulty in recovering the regression function for the fractional variable, and there is no need to use ad hoc transformations to handle data at the extreme values of zero and one. We also offer some new, robust specification tests by nesting the logit or probit function in a more general functional form. We apply these methods to a data set of employee participation rates in 401(k) pension plans. Copyright 1996 by John Wiley & Sons, Ltd.

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  • Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
    • Handle: RePEc:jae:japmet:v:11:y:1996:i:6:p:619-32
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    References listed on IDEAS

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    1. Davidson, Russell & MacKinnon, James G., 1984. "Convenient specification tests for logit and probit models," Journal of Econometrics, Elsevier, vol. 25(3), pages 241-262, July.
    2. repec:cup:etheor:v:9:y:1993:i:1:p:94-113 is not listed on IDEAS
    3. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Theory," Econometrica, Econometric Society, vol. 52(3), pages 681-700, May.
    4. Gurmu, Shiferaw & Trivedi, Pravin K., 1993. "Variable Augmentation Specification Tests in the Exponential Family," Econometric Theory, Cambridge University Press, vol. 9(1), pages 94-113, January.
    5. Wooldridge, Jeffrey M., 1991. "Specification testing and quasi-maximum- likelihood estimation," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 29-55.
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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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