Empirical Likelihood-Based Inference in Conditional Moment Restriction Models
Gautam Tripathi () and
Econometrica, 2004, vol. 72, issue 6, 1667-1714
This paper proposes an asymptotically efficient method for estimating models with conditional moment restrictions. Our estimator generalizes the maximum empirical likelihood estimator (MELE) of Qin and Lawless (1994). Using a kernel smoothing method, we efficiently incorporate the information implied by the conditional moment restrictions into our empirical likelihood-based procedure. This yields a one-step estimator which avoids estimating optimal instruments. Our likelihood ratio-type statistic for parametric restrictions does not require the estimation of variance, and achieves asymptotic pivotalness implicitly. The estimation and testing procedures we propose are normalization invariant. Simulation results suggest that our new estimator works remarkably well in finite samples. Copyright The Econometric Society 2004.
References: Add references at CitEc
Citations: View citations in EconPapers (63) Track citations by RSS feed
Downloads: (external link)
http://hdl.handle.net/10.1111/j.1468-0262.2004.00550.x link to full text (text/html)
Access to full text is restricted to subscribers.
Working Paper: Empirical Likelihood-Based Inference in Conditional Moment Restriction Models (2001)
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:ecm:emetrp:v:72:y:2004:i:6:p:1667-1714
Ordering information: This journal article can be ordered from
https://www.economet ... ordering-back-issues
Access Statistics for this article
Econometrica is currently edited by Daron Acemoglu
More articles in Econometrica from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing ().