A Note on the Bandwidth Choice When the Null Hypothesis is Semiparametric

Publicado en

  • Revista de Economía del Rosario


  • 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.

fecha de publicación

  • 2005

Líneas de investigación

  • Additive Models
  • Bootstrap
  • Bootstrap Test
  • Kernel Smoothing
  • Nonparametric Regression