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Generalized Semiparametric Binary Prediction

  • Jeff Racine

    ()

    (Department of Economics, University of South Florida)

This paper proposes a semiparametric approach to the estimation of ¡®generalized¡¯ binary choice models. A ¡®generalized¡¯ binary choice model is one with separate indices for each conditioning variable which constitutes a generalization of the standard single-index approach typically employed in applied work. The choice probability distribution is therefore a joint distribution across these indices as opposed to the typical univariate distribution on a scalar index commonly found in applied work. Interest lies in estimating choice probabilities and the gradient of choice probabilities with respect to the conditioning information, and these are estimated nonparametrically using the method of kernels. A data-driven cross-validatory method for bandwidth selection and index-parameter estimation is proposed for maximization of the nonparametric likelihood function. The functional form of the indices enters this nonparametric likelihood function thereby permitting data-driven determination of the index functions in addition to the shape of the joint cumulative distribution function itself. Applications are considered.

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Article provided by Society for AEF in its journal Annals of Economics and Finance.

Volume (Year): 3 (2002)
Issue (Month): 1 (May)
Pages: 117-134

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Handle: RePEc:cuf:journl:y:2002:v:3:i:1:p:117-134
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  1. Ichimura, H. & Thompson, S., 1993. "Maximum Likelihood Estimation of a Binary Choice Model with Random Coefficients of Unknown Distributions," Papers 268, Minnesota - Center for Economic Research.
  2. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
  3. Chen, Heng Z. & Randall, Alan, 1997. "Semi-nonparametric estimation of binary response models with an application to natural resource valuation," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 323-340.
  4. Lee, Lung-fei, 1995. "Semiparametric maximum likelihood estimation of polychotomous and sequential choice models," Journal of Econometrics, Elsevier, vol. 65(2), pages 381-428, February.
  5. Cosslett, Stephen R, 1983. "Distribution-Free Maximum Likelihood Estimator of the Binary Choice Model," Econometrica, Econometric Society, vol. 51(3), pages 765-82, May.
  6. Amemiya, Takeshi, 1981. "Qualitative Response Models: A Survey," Journal of Economic Literature, American Economic Association, vol. 19(4), pages 1483-1536, December.
  7. McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457 Elsevier.
  8. Picone, Gabriel A. & Butler, J.S., 2000. "Semiparametric Estimation Of Multiple Equation Models," Econometric Theory, Cambridge University Press, vol. 16(04), pages 551-575, August.
  9. Klein, R.W. & Spady, R.H., 1991. "An Efficient Semiparametric Estimator for Binary Response Models," Papers 70, Bell Communications - Economic Research Group.
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