Gradient-based smoothing parameter selection for nonparametric regression estimation

Daniel Henderson (), Qi Li, Christopher Parmeter and Shuang Yao

Journal of Econometrics, 2015, vol. 184, issue 2, 233-241

Abstract: Estimating gradients is of crucial importance across a broad range of applied economic domains. Here we consider data-driven bandwidth selection based on the gradient of an unknown regression function. This is a difficult problem given that direct observation of the value of the gradient is typically not observed. The procedure developed here delivers bandwidths which behave asymptotically as though they were selected knowing the true gradient. Simulated examples showcase the finite sample attraction of this new mechanism and confirm the theoretical predictions.

Keywords: Gradient estimation; Kernel smoothing; Least squares cross validation (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (14)

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Working Paper: Gradient Based Smoothing Parameter Selection for Nonparametric Regression Estimation (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:184:y:2015:i:2:p:233-241

DOI: 10.1016/j.jeconom.2014.09.007

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