Gradient-based smoothing parameter selection for nonparametric regression estimation

Daniel J. Henderson, Qi Li, Christopher F. Parmeter, Shuang Yao

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)233-241
Number of pages9
JournalJournal of Econometrics
Issue number2
StatePublished - Feb 1 2015


  • Gradient estimation
  • Kernel smoothing
  • Least squares cross validation

ASJC Scopus subject areas

  • Economics and Econometrics


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