Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5096931 | Journal of Econometrics | 2010 | 13 Pages |
Abstract
Cost 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.
Keywords
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Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Peter Hall, Adonis Yatchew,