Professor Anurag Banerjee a.n.banerjee@durham.ac.uk
Professor
A method of estimating the average derivative
Banerjee, A.N.
Authors
Abstract
We derive a simple semi-parametric estimator of the “direct” Average Derivative, δ=E(D[m(x)]), where m(x) is the regression function and S, the support of the density of x is compact. We partition S into disjoint bins and the local slope D[m(x)] within these bins is estimated by using ordinary least squares. Our average derivative estimate , is then obtained by taking the weighted average of these least squares slopes. We show that this estimator is asymptotically normally distributed. We also propose a consistent estimator of the variance of . Using Monte-Carlo simulation experiments based on a censored regression model (with Tobit Model as a special case) we produce small sample results comparing our estimator with the Härdle–Stoker [1989. Investigating smooth multiple regression by the method of average derivatives. Journal of American Statistical Association 84, 408, 986–995] method. We conclude that performs better that the Härdle–Stoker estimator for bounded and discontinuous covariates.
Citation
Banerjee, A. (2007). A method of estimating the average derivative. Journal of Econometrics, 136(1), 65-88. https://doi.org/10.1016/j.jeconom.2005.07.010
Journal Article Type | Article |
---|---|
Online Publication Date | Aug 24, 2005 |
Publication Date | 2007-01 |
Deposit Date | Mar 16, 2007 |
Journal | Journal of Econometrics |
Print ISSN | 0304-4076 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 136 |
Issue | 1 |
Pages | 65-88 |
DOI | https://doi.org/10.1016/j.jeconom.2005.07.010 |
Keywords | Semi-parametric estimation, Average derivative estimator, Linear regression. |
Public URL | https://durham-repository.worktribe.com/output/1604438 |
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