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A Derivative Based Estimator for Semiparametric Index Models

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  • Donkers, A.C.D.
  • Schafgans, M.

Abstract

This paper proposes a semiparametric estimator for single- and multiple index models. It provides an extension of the average derivative estimator to the multiple index model setting. The estimator uses the average of the outer product of derivatives and is shown to be root-N consistent and asymptotically normal. Unlike the average derivative estimator, our estimator still works in the single-index setting when the expected derivative is zero (symmetry). Compared to other estimators for multiple index models, the proposed estimator has the advantage of ease of computation. While many econometric models can be regarded as multiple index models with known number of indices, our estimator in addition provides for a natural framework within which to test for the number of indices required.
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Suggested Citation

  • Donkers, A.C.D. & Schafgans, M., 2003. "A Derivative Based Estimator for Semiparametric Index Models," Other publications TiSEM 92ffa14b-de76-4309-8bee-1, Tilburg University, School of Economics and Management.
  • Handle: RePEc:tiu:tiutis:92ffa14b-de76-4309-8bee-19874a011812
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    References listed on IDEAS

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    1. Serge Darolles & Christian Gourieroux & Joanna Jasiak, 2001. "Compound Autoregressive Models," Working Papers 2001-21, Center for Research in Economics and Statistics.
    2. 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.
    3. Yingcun Xia & Howell Tong & W. K. Li & Li‐Xing Zhu, 2002. "An adaptive estimation of dimension reduction space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 363-410, August.
    4. Picone, Gabriel A. & Butler, J.S., 2000. "Semiparametric Estimation Of Multiple Equation Models," Econometric Theory, Cambridge University Press, vol. 16(4), pages 551-575, August.
    5. Collomb, Gérard & Härdle, Wolfgang, 1986. "Strong uniform convergence rates in robust nonparametric time series analysis and prediction: Kernel regression estimation from dependent observations," Stochastic Processes and their Applications, Elsevier, vol. 23(1), pages 77-89, October.
    6. Barnett,William A. & Powell,James & Tauchen,George E. (ed.), 1991. "Nonparametric and Semiparametric Methods in Econometrics and Statistics," Cambridge Books, Cambridge University Press, number 9780521424318.
    7. Robin, Jean-Marc & Smith, Richard J., 2000. "Tests Of Rank," Econometric Theory, Cambridge University Press, vol. 16(2), pages 151-175, April.
    8. P. M. Robinson, 1989. "Hypothesis Testing in Semiparametric and Nonparametric Models for Econometric Time Series," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 56(4), pages 511-534.
    9. Barnett,William A. & Powell,James & Tauchen,George E. (ed.), 1991. "Nonparametric and Semiparametric Methods in Econometrics and Statistics," Cambridge Books, Cambridge University Press, number 9780521370905.
    10. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-531, May.
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    Cited by:

    1. Bas Donkers & Marcia M Schafgans, 2005. "A method of moments estimator for semiparametric index models," STICERD - Econometrics Paper Series 493, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Donald, Stephen G. & Fortuna, Natércia & Pipiras, Vladas, 2007. "On Rank Estimation In Symmetric Matrices: The Case Of Indefinite Matrix Estimators," Econometric Theory, Cambridge University Press, vol. 23(6), pages 1217-1232, December.

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