On the Asymptotic Efficiency of Directional Models Checks for Regression

Miguel A. Delgado () and Juan Carlos Escanciano
Additional contact information
Miguel A. Delgado: Universidad Carlos III de Madrid
Juan Carlos Escanciano: Indiana University

Chapter Chapter 5 in From Statistics to Mathematical Finance, 2017, pp 71-87 from Springer

Abstract: Abstract In a landmark paper, Stute (1997), Annals of Statistics) introduced smooth and directional tests for homoskedastic regression models based on weighted averages of estimated principal components of the CUSUM of residual concomitants process. This article shows that Stute’s (1997) directional test is asymptotically uniformly most powerful in a semiparametric context, considering the joint distribution of the regression error term and the covariate as a nuisance parameter. Moreover, the directional test is shown to be asymptotically equivalent to the classical t-ratio under homoskedasticity. This article also studies the heteroskedastic case, where the directional test based on the CUSUM of standarized residual concomitants (Stute et al. (1998), Annals of Statistics) is asymptotically equivalent to the semiparametric efficient test, which is the t-ratio using the generalized least squares estimator.

Keywords: Neyman-Pearson lemma; Functional likelihood ratio; Semiparametric efficiency; Effective score; Empirical processes theory; C12; C14 (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-50986-0_5

Ordering information: This item can be ordered from
http://www.springer.com/9783319509860

DOI: 10.1007/978-3-319-50986-0_5

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().