Nonparametric least squares estimation in derivative families
AbstractCost function estimation often involves data on a function and a family of its derivatives. Such data can substantially improve convergence rates of nonparametric estimators. We propose series-type estimators which incorporate the various derivative data into a single nonparametric least-squares procedure. Convergence rates are obtained and it is shown that for low-dimensional cases, much of the beneficial impact is realized even if only data on ordinary first-order partials are available. In instances where root-n consistency is attained, smoothing parameters can often be chosen very easily, without resort to cross-validation. Simulations and an illustration of cost function estimation are included.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 157 (2010)
Issue (Month): 2 (August)
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Web page: http://www.elsevier.com/locate/jeconom
Nonparametric regression Cost and factor demand estimation Partial derivative data Curse of dimensionality Dimension reduction Rates of convergence Orthogonal series methods Cross-validation Smoothing parameter selection;
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