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Gradient-based smoothing parameter selection for nonparametric regression estimation |
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| Authors: | Daniel J. Henderson Qi Li Christopher F. Parmeter Shuang Yao |
| Affiliation: | 1. Department of Economics, Finance and Legal Studies, University of Alabama, United States;2. Department of Economics, Texas A&M University, United States;3. ISEM, Capital University of Economics & Business, PR China;4. Department of Economics, University of Miami, United States;5. Economics and Management School, Wuhan University, PR China |
| 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 |
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