Blockwise generalized empirical likelihood inference for non-linear dynamic moment conditions models
Francesco Bravo
Econometrics Journal, 2009, vol. 12, issue 2, 208-231
Abstract:
This paper shows how the blockwise generalized empirical likelihood method can be used to obtain valid asymptotic inference in non-linear dynamic moment conditions models for possibly non-stationary weakly dependent stochastic processes. The results of this paper can be used to construct test statistics for overidentifying moment restrictions, for additional moments, and for parametric restrictions expressed in mixed implicit and constraint form. Monte Carlo simulations seem to suggest that some of the proposed test statistics have competitive finite sample properties. Copyright © 2009 The Author(s). Journal compilation © Royal Economic Society 2009
Date: 2009
References: Add references at CitEc
Citations: View citations in EconPapers (8)
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:ect:emjrnl:v:12:y:2009:i:2:p:208-231
Ordering information: This journal article can be ordered from
http://www.ectj.org
Access Statistics for this article
Econometrics Journal is currently edited by Richard J. Smith, Oliver Linton, Pierre Perron, Jaap Abbring and Marius Ooms
More articles in Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing () and Christopher F. Baum ().