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Perspectives on Nonparametric and Semiparametric Modeling

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  • Adonis Yatchew

Abstract

Nonparametric regression techniques hold out the promise of more flexible modeling of data in many areas of physical, biological and social sciences. However, their use is hampered by the “curse of dimensionality†which imposes enormous data requirements as the number of explanatory variables increases. After summarizing two of the most commonly used methods for mitigating the "curse†, this paper outlines a new approach which exploits data on derivatives. In economics, such circumstances arise in the joint estimation of cost and factor demand functions, or when production function data are combined with data on factor prices. The ideas are illustrated using empirical examples from energy economics.

Suggested Citation

  • Adonis Yatchew, 2008. "Perspectives on Nonparametric and Semiparametric Modeling," The Energy Journal, , vol. 29(1_suppl), pages 17-30, June.
    • Handle: RePEc:sae:enejou:v:29:y:2008:i:1_suppl:p:17-30
    DOI: 10.5547/ISSN0195-6574-EJ-Vol29-NoSI-2
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    References listed on IDEAS

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    1. Klein, Roger W & Spady, Richard H, 1993. "An Efficient Semiparametric Estimator for Binary Response Models," Econometrica, Econometric Society, vol. 61(2), pages 387-421, March.
    2. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, September.
    3. Yatchew,Adonis, 2003. "Semiparametric Regression for the Applied Econometrician," Cambridge Books, Cambridge University Press, number 9780521812832.
    4. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, September.
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