Consistent Pseudo-Maximum Likelihood Estimators

Christian Gourieroux, Alain Monfort and Eric Renault

Annals of Economics and Statistics, 2017, issue 125-126, 187-218

Abstract: The development of the literature on the pseudo maximum likelihood (PML) estimators would not have been so efficient without the modern proof of consistency of extremum estimators introduced at the end of the sixties by E. Malinvaud and R. Jennrich. We discuss this proof and replace it in an historical perspective. In this paper we also provide a survey of the literature on consistent (PML) estimators. We emphasize the role of the white noise assumptions on the set of pseudo distributions leading to consistent estimators. The stronger these assumptions, the larger the set of consistent PML estimators. We also illustrate the importance of these PML approaches in big data environment.

Keywords: Pseudo-Likelihood; Composite Pseudo-Likelihood; Consistency; Big Data; ARCH Model; Normalized Data; Lie Group (search for similar items in EconPapers)
JEL-codes: C13 C14 C51 C55 (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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http://www.jstor.org/stable/10.15609/annaeconstat2009.125-126.0187 (text/html)

Related works:
Working Paper: Consistent Pseudo-Maximum Likelihood Estimators (2017)
Working Paper: Consistent Pseudo-Maximum Likelihood Estimators (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:adr:anecst:y:2017:i:125-126:p:187-218

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