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Perspectives on nonparametric and Semiparametric Modeling

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.

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Energy Specializations: Energy Modeling – Energy Data, Modeling, and Policy Analysis; Energy Modeling – Other

JEL Codes:
E61 - Policy Objectives; Policy Designs and Consistency; Policy Coordination
C59 - Econometric Modeling: Other

Keywords: Nonparametric and semiparametric modeling, Curse of dimensionality, Energy economics

DOI: 10.5547/ISSN0195-6574-EJ-Vol29-NoSI-2

Published in Volume 29, Special Issue of The Quarterly Journal of the IAEE's Energy Economics Education Foundation.