Bandwidth Selection in Nonparametric Kernel Testing
AbstractWe propose a sound approach to bandwidth selection in nonparametric kernel testing. The main idea is to find an Edgeworth expansion of the asymptotic distribution of the test concerned. Due to the involvement of a kernel bandwidth in the leading term of the Edgeworth expansion, we are able to establish closedÃâform expressions to explicitly represent the leading terms of both the size and power functions and then determine how the bandwidth should be chosen according to certain requirements for both the size and power functions. For example, when a significance level is given, we can choose the bandwidth such that the power function is maximized while the size function is controlled by the significance level. Both asymptotic theory and methodology are established. In addition, we develop an easy implementation procedure for the practical realization of the established methodology and illustrate this on two simulated examples and a real data example.
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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of the American Statistical Association.
Volume (Year): 103 (2008)
Issue (Month): 484 ()
Contact details of provider:
Web page: http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main
Other versions of this item:
- Jiti Gao & Irene Gijbels, 2009. "Bandwidth Selection in Nonparametric Kernel Testing," School of Economics Working Papers 2009-01, University of Adelaide, School of Economics.
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Lee, Sokbae & Song, Kyungchul & Whang, Yoon-Jae, 2013.
"Testing functional inequalities,"
Journal of Econometrics,
Elsevier, vol. 172(1), pages 14-32.
- Taoufik Bouezmarni & Jeroen Rombouts & Abderrahim Taamouti, 2009.
"A Nonparametric Copula Based Test for Conditional Independence with Applications to Granger Causality,"
CIRANO Working Papers
- Taoufik Bouezmarni & Jeroen V.K. Rombouts & Abderrahim Taamouti, 2009. "A Nonparametric Copula Based Test for Conditional Independence with Applications to Granger Causality," Cahiers de recherche 0927, CIRPEE.
- BOUEZMARNI, Taoufik & ROMBOUTS, Jeroen & TAAMOUTI, Abderrahim, 2009. "A nonparametric copula based test for conditional independence with applications to Granger causality," CORE Discussion Papers 2009041, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Taoufik Bouezmarni & Jeroen V. K. Rombouts & Abderrahim Taamouti, 2009. "A nonparametric copula based test for conditional independence with applications to granger causality," Economics Working Papers we093419, Universidad Carlos III, Departamento de Economía.
- Bouezmarni, Taoufik & Rombouts, Jeroen V. K. & Taamouti, Abderrahim, . "A nonparametric copula based test for conditional independence with applications to granger causality," Open Access publications from Universidad Carlos III de Madrid info:hdl:10016/4491, Universidad Carlos III de Madrid.
- Song Xi Chen & Jiti Gao, 2010. "Simultaneous Testing of Mean and Variance Structures in Nonlinear Time Series Models," School of Economics Working Papers 2010-28, University of Adelaide, School of Economics.
- Delsol, Laurent & Ferraty, Frédéric & Vieu, Philippe, 2011. "Structural test in regression on functional variables," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 422-447, March.
- Xu, Ke-Li, 2013. "Powerful tests for structural changes in volatility," Journal of Econometrics, Elsevier, vol. 173(1), pages 126-142.
- Jiti Gao & Maxwell King, 2011. "A New Test in Parametric Linear Models against Nonparametric Autoregressive Errors," Monash Econometrics and Business Statistics Working Papers 20/11, Monash University, Department of Econometrics and Business Statistics.
- Politis, D N, 2009. "Higher-Order Accurate, Positive Semi-definite Estimation of Large-Sample Covariance and Spectral Density Matrices," University of California at San Diego, Economics Working Paper Series qt66w826hz, Department of Economics, UC San Diego.
- Jiti Gao, 2012. "Identification, Estimation and Specification in a Class of Semiparametic Time Series Models," Monash Econometrics and Business Statistics Working Papers 6/12, Monash University, Department of Econometrics and Business Statistics.
- Gao, Jiti, 2012. "Identification, Estimation and Specification in a Class of Semi-Linear Time Series Models," MPRA Paper 39256, University Library of Munich, Germany, revised 14 May 2012.
- George Athanasopoulos & Minfeng Deng & Gang Li & Haiyan Song, 2013. "Domestic and outbound tourism demand in Australia: a System-of-Equations Approach," Monash Econometrics and Business Statistics Working Papers 6/13, Monash University, Department of Econometrics and Business Statistics.
- Sun, Yixiao, 2013. "Let's Fix It: Fixed-b Asymptotics versus Small-b Asymptotics in Heteroscedasticity and Autocorrelation Robust Inference," University of California at San Diego, Economics Working Paper Series qt8x8307rz, Department of Economics, UC San Diego.
- Sokbae Lee & Yoon-Jae Whang, 2009.
"Nonparametric Tests of Conditional Treatment Effects,"
Cowles Foundation Discussion Papers
1740, Cowles Foundation for Research in Economics, Yale University.
- Sokbae 'Simon' Lee & Yoon-Jae Whang, 2009. "Nonparametric tests of conditional treatment effects," CeMMAP working papers CWP36/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- J. M. Krief, . "Two Stage Semi Parametric Quantile Regression," Departmental Working Papers 2009-05, Department of Economics, Louisiana State University.
- Patrick Saart & Jiti Gao, 2012. "Semiparametric Methods in Nonlinear Time Series Analysis: A Selective Review," Monash Econometrics and Business Statistics Working Papers 21/12, Monash University, Department of Econometrics and Business Statistics.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.