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A note on the Bandwidth choice when the null hypothesis is semiparametric

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  • Jorge Barrientos Marín

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Abstract

This 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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File URL: http://www.urosario.edu.co/FASE1/economia/documentos/v8n2_Barrientos.pdf
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Article provided by UNIVERSIDAD DEL ROSARIO in its journal REVISTA DE ECONOMÍA DEL ROSARIO.

Volume (Year): (2005)
Issue (Month): ()
Pages:

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Handle: RePEc:col:000151:001924

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Keywords: additive models; bootstrap; bootstrap test; kernel smoothing; nonparametric;

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  1. 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.
  2. 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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