Rate-optimal data-driven specification testing in regression models

Emmanuel Guerre () and Pascal Lavergne

Econometrics from EconWPA

Abstract: We propose a general procedure for testing that a regression function has a prescribed parametric form. We allow for multivariate regressors, non-normal errors and heteroscedasticity of unknown form. The test relies upon a nonparametric linear estimation method, such as a sieves expansion or the kernel method. The choice of the smoothing parameter is data-driven. Under the null hypothesis, the asymptotic distribution of the test statistic is the standard normal distribution. Use of bootstrap critical values is formally justified. The test is shown to be adaptive and rate-optimal in the minimax sense. Detection of Pitman-type local alternatives is also studied.

Keywords: rate-optimal; nonparametric; data-driven; specification; test (search for similar items in EconPapers)
JEL-codes: C12 C14 C52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2001-07-12
Note: Type of Document - pdf; prepared on IBM PC ; to print on HP;
View list of references

Downloads: (external link)
http://129.3.20.41/eps/em/papers/0107/0107001.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:wpa:wuwpem:0107001

Access Statistics for this paper

More papers in Econometrics from EconWPA
Series data maintained by EconWPA ().