Adapting Kernel Estimation to Uncertain Smoothness
AbstractFor local and average kernel based estimators, smoothness conditions ensure that the kernel order determines the rate at which the bias of the estimator goes to zero and thus allows the econometrician to control the rate of convergence. In practice, even with smoothness the estimation errors may be substantial and sensitive to the choice of the bandwidth and kernel. For distributions that do not have sufficient smoothness asymptotic theory may importantly differ from standard; for example, there may be no bandwidth for which average estimators attain root-n consistency. We demonstrate that non-convex combinations of estimators computed for different kernel/bandwidth pairs can reduce the trace of asymptotic mean square error relative even to the optimal kernel/bandwidth pair. Our combined estimator builds on these results. To construct it we provide new general estimators for degree of smoothness, optimal rate and for the biases and covariances of estimators. We show that a bootstrap estimator is consistent for the variance of local estimators but exhibits a large bias for the average estimators; a suitable adjustment is provided.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number /2011/557.
Date of creation: Apr 2011
Date of revision:
Contact details of provider:
Web page: http://sticerd.lse.ac.uk/_new/publications/default.asp
Nonparametric estimation; kernel based estimator; combined stimator; variance bootstrap.;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- HARDLE, Wolfgang & HILDENBRAND, Werner & JERISON, Michael, .
"Empirical evidence on the law of demand,"
CORE Discussion Papers RP
-968, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Wolfgang Härdle & Werner Hildenbrand & Michael Jerison, 1989. "Empirical Evidence on the Law of Demand," Discussion Paper Serie A 264a, University of Bonn, Germany.
- Haerdle,W. Hildenbrand,W. Jerison,M., 1988. "Empirical evidence on the law of demand," Discussion Paper Serie A 193, University of Bonn, Germany.
- Millimet, Daniel & Henderson, Daniel, 2006.
"Is Gravity Linear?,"
Departmental Working Papers
0517, Southern Methodist University, Department of Economics.
- Calabrese, Raffaella & Zenga, Michele, 2010. "Bank loan recovery rates: Measuring and nonparametric density estimation," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 903-911, May.
- DiNardo, John & Tobias, Justin, 2001.
"Nonparametric Density and Regression Estimation,"
Staff General Research Papers
12020, Iowa State University, Department of Economics.
- Richard Blundell & Alan Duncan, 1998. "Kernel Regression in Empirical Microeconomics," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 62-87.
- Huynh, Kim P. & Jacho-Chávez, David T., 2009. "Growth and governance: A nonparametric analysis," Journal of Comparative Economics, Elsevier, vol. 37(1), pages 121-143, March.
- Matias D. Cattaneo & Richard K. Crump & Michael Jansson, 2010.
"Bootstrapping Density-Weighted Average Derivatives,"
CREATES Research Papers
2010-23, School of Economics and Management, University of Aarhus.
- Matias D. Cattaneo & Richard K. Crump & Michael Jansson, 2010. "Bootstrapping density-weighted average derivatives," Staff Reports 452, Federal Reserve Bank of New York.
- Yulia Kotlyarova & Victoria Zinde-Walsh, 2006. "Robust Kernel Estimator For Densities Of Unknown," Departmental Working Papers 2005-05, McGill University, Department of Economics.
- Kotlyarova, Yulia & Zinde-Walsh, Victoria, 2006.
"Non- and semi-parametric estimation in models with unknown smoothness,"
Elsevier, vol. 93(3), pages 379-386, December.
- Yulia Kotlyarova & Victoria Zinde-Walsh, 2006. "Non And Semi-Parametric Estimation In Models With Unknown Smoothness," Departmental Working Papers 2006-15, McGill University, Department of Economics.
- Hansen, Bruce E., 2005. "Exact Mean Integrated Squared Error Of Higher Order Kernel Estimators," Econometric Theory, Cambridge University Press, vol. 21(06), pages 1031-1057, December.
- Abadir, Karim M. & Lawford, Steve, 2004. "Optimal asymmetric kernels," Economics Letters, Elsevier, vol. 83(1), pages 61-68, April.
- Donkers, Bas & Schafgans, Marcia, 2008. "Specification And Estimation Of Semiparametric Multiple-Index Models," Econometric Theory, Cambridge University Press, vol. 24(06), pages 1584-1606, December.
- Richard Blundell & Alan Duncan & Krishna Pendakur, 1998. "Semiparametric estimation and consumer demand," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(5), pages 435-461.
- Marcia M. A. Schafgans & Victoria Zinde-Walsh, 2010. "Smoothness adaptive average derivative estimation," Econometrics Journal, Royal Economic Society, vol. 13(1), pages 40-62, 02.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.