Smoothness Adaptive Average Derivative Estimation

31 Pages Posted: 13 May 2009

See all articles by Marcia Schafgans

Marcia Schafgans

London School of Economics & Political Science (LSE)

Victoria Zinde‐Walsh

McGill University - Department of Economics

Multiple version iconThere are 2 versions of this paper

Date Written: August 2008


Many important models, such as index models widely used in limited dependent variables, partial linear models and nonparametric demand studies utilize estimation of average derivatives (sometimes weighted) of the conditional mean function. Asymptotic results in the literature focus on situations where the ADE converges at parametric rates (as a result of averaging); this requires making stringent assumptions on smoothness of the underlying density; in practice such assumptions may be violated. We extend the existing theory by relaxing smoothness assumptions. We consider both the possibility of lack of smoothness and lack of precise knowledge of degree of smoothness and propose an estimation strategy that produces the best possible rate without a priori knowledge of degree of density smoothness. The new combined estimator is a linear combination of estimators corresponding to different bandwidth/kernel choices that minimizes the trace of the part of the estimated asymptotic mean squared error that depends on the bandwidth. Estimation of the components of the AMSE, of the optimal bandwidths, selection of the set of bandwidths and kernels are discussed. Monte Carlo results for density weighted ADE confirm good performance of the combined estimator.

JEL Classification: C14

Suggested Citation

Schafgans, Marcia and Zinde-Walsh, Victoria, Smoothness Adaptive Average Derivative Estimation (August 2008). LSE STICERD Research Paper No. EM529, Available at SSRN:

Marcia Schafgans (Contact Author)

London School of Economics & Political Science (LSE) ( email )

Houghton Street
London, WC2A 2AE
United Kingdom

Victoria Zinde-Walsh

McGill University - Department of Economics ( email )

855 Sherbrooke Street West
Montreal, QC H3A 2T7
514-398-4834 (Phone)
514-398-4938 (Fax)

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics