Testing conditional moment restriction models using empirical likelihood

Efficient estimation of models with conditional moment restrictions containing unknown functions

Yves G Berger

The Econometrics Journal, 2022, vol. 25, issue 2, 384-403

Abstract: SummaryAn empirical likelihood test is proposed for parameters of models defined by conditional moment restrictions, such as models with nonlinear endogenous covariates, with and without heteroscedastic errors and non-separable transformation models. The number of empirical likelihood constraints is given by the size of the parameter, unlike alternative semi-parametric approaches. We show that the empirical likelihood ratio test is asymptotically pivotal, without explicit studentization. A simulation study shows that the observed size is close to the nominal level, unlike alternative empirical likelihood approaches. It also offers a major advantage over two-stage least-squares, because the relationship between endogenous and instrumental variables does not need to be known. An empirical likelihood model specification test is also proposed.

Keywords: Endogenous covariates; Fourier transform; heteroscedasticity; model specification; two-stage least-squares (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/ectj/utab032 (application/pdf)
Access to full text is restricted to subscribers.

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:oup:emjrnl:v:25:y:2022:i:2:p:384-403.

Access Statistics for this article

The Econometrics Journal is currently edited by Jaap Abbring

More articles in The Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().