A note on the Bandwidth choice when the null hypothesis is semiparametric
AbstractThis work presents a tool for the additivity test. The additive model is widely used for parametric and semiparametric modeling of economic data. The additivity hypothesis is of interest because it is easy to interpret and produces reasonably fast convergence rates for non-parametric estimators. Another advantage of additive models is that they allow attacking the problem of the curse of dimensionality that arises in non- parametric estimation. Hypothesis testing is based in the well-known bootstrap residual process. In nonparametric testing literature, the dominant idea is that bandwidth utilized to produce bootstrap sample should be bigger that bandwidth for estimating model under null hypothesis. However, there is no hint so far about how to choose such bandwidth in practice. We will discuss a first step to find some rule of thumb to choose bandwidth in that context. Our suggestions are accompanied by simulation studies.
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Bibliographic InfoArticle provided by UNIVERSIDAD DEL ROSARIO in its journal REVISTA DE ECONOMÍA DEL ROSARIO.
Volume (Year): (2005)
Issue (Month): ()
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additive models; bootstrap; bootstrap test; kernel smoothing; nonparametric;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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.:
- Sperlich, Stefan & Tj stheim, Dag & Yang, Lijian, 2002.
"Nonparametric Estimation And Testing Of Interaction In Additive Models,"
Econometric Theory, Cambridge University Press,
Cambridge University Press, vol. 18(02), pages 197-251, April.
- Sperlich, Stefan & Tjøstheim, Dag & Yang, Lijian, 1998. "Nonparametric estimation and testing of interaction in additive models," SFB 373 Discussion Papers, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes 1998,14, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- Gozalo, Pedro L. & Linton, Oliver B., 2001. "Testing additivity in generalized nonparametric regression models with estimated parameters," Journal of Econometrics, Elsevier, Elsevier, vol. 104(1), pages 1-48, August.
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