A GMM estimator asymptotically more efficient than OLS and WLS in the presence of heteroskedasticity of unknown form

Cuicui Lu and Jeffrey Wooldridge

Applied Economics Letters, 2020, vol. 27, issue 12, 997-1001

Abstract: We propose a generalized method of moments (GMM) estimator, where our specific moment conditions, where our specific moment conditions ensure that the GMM estimator is asymptotically at least as efficient as ordinary least squares (OLS) and whatever competing weighted least squares (WLS) we wish to consider. With a popular exponential model of heteroskedasticity, our new GMM estimator performs significantly better than OLS or WLS. In an empirical application to a financial wealth equation, we show that the efficiency gains can be nontrivial with real data.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2019.1657228 (text/html)
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:taf:apeclt:v:27:y:2020:i:12:p:997-1001

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20

DOI: 10.1080/13504851.2019.1657228

Access Statistics for this article

Applied Economics Letters is currently edited by Anita Phillips

More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst (chris.longhurst@tandf.co.uk).