论文简介： 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.
本文刊登于Journal of Econometrics (184 (2015) 233-241). Journal of Econometrics是计量经济学领域的国际顶尖期刊，现任主编为计量经济学界的顶尖学者Yacine Ait-Sahalia， Jianqing Fan， Han Hong， Oliver Linton。主要刊发计量经济学理论及应用的学术研究成果。五年影响因子为2.263， 为太阳集团A期刊。
本文第一作者：Daniel J. Henderson (University of Alabama)
二作：Qi Li (Texas A&M University & Capital University of Economics and Business)
三作：Christopher F. Parmeter (University of Miami)
通讯作者：Shuang Yao (Wuhan University)
