Asymptotically Distribution-Free Goodness-of-Fit Testing: A Unifying View
Bo Li ()
Econometric Reviews, 2009, vol. 28, issue 6, 632-657
We outline a general paradigm for constructing asymptotically distribution-free (ADF) goodness-of-fit tests, which can be regarded as a generalization of Khmaladze (1993). This is achieved by a nonorthogonal projection of a class of functions onto the ortho-complement of the extended tangent space (ETS) associated with the null hypothesis. In parallel with the work of Bickel et al. (2006), we obtain transformed empirical processes (TEP) which are the building blocks for constructing omnibus tests such as the usual Kolmogorov-Smirnov type tests and Cramer-von Mise type tests, as well as Portmanteau tests and directional tests. The critical values can be tabulated due to the ADF property. All the tests are capable of detecting local (Pitman) alternative at the root-n scale. We shall illustrate the framework in several examples, mostly in regression model specification testing.
Keywords: ADF; Empirical process; Goodness-of-fit tests; Martingale transform; Semiparametric; Tangent space (search for similar items in EconPapers)
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:28:y:2009:i:6:p:632-657
Ordering information: This journal article can be ordered from
Access Statistics for this article
Econometric Reviews is currently edited by Dr. Essie Maasoumi
More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().