Consistent Pseudo-Maximum Likelihood Estimators

Christian Gourieroux, Alain Monfort and Eric Renault ()
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Eric Renault: Brown University

No 2016-33, Working Papers from Center for Research in Economics and Statistics

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

Keywords: Pseudo-Likelihood; Composite Pseudo-Likelihood; Consistency; Big Data; ARCH Model; Normalized Data; Lie Group. (search for similar items in EconPapers)
Pages: 42
Date: 2016-09
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Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Consistent Pseudo-Maximum Likelihood Estimators (2017)
Working Paper: Consistent Pseudo-Maximum Likelihood Estimators (2017)
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