Consistent Pseudo-Maximum Likelihood Estimators and Groups of Transformations

Christian Gourieroux, Alain Monfort and Jean-Michel Zakoian

No 2018-08, Working Papers from Center for Research in Economics and Statistics

Abstract: In a transformation model yt = c[a(xt, ß),ut], where the errors ut are i.i.d and independent of the explanatory variables xt, the parameters can be estimated by a pseudo-maximum likelihood (PML) method, that is, by using a misspecified distribution of the errors, but the PML estimator of ß is in general not consistent. We explain in this paper how to nest the initial model in an identified augmented model with more parameters in order to derive consistent PML estimators of appropriate functions of parameter ß. The usefulness of the consistency result is illustrated by examples of systems of nonlinear equations, conditionally heteroskedastic models, stochastic volatility, or models with spatial interactions.

Keywords: Pseudo-Maximum Likelihood; Transformation Model; Identification; Consistency; Stochastic Volatility; Conditional Heteroskedasticity; Spatial Interactions (search for similar items in EconPapers)
Pages: 24 pages
Date: 2018-06-01
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: Consistent Pseudo‐Maximum Likelihood Estimators and Groups of Transformations (2019)
Working Paper: Consistent Pseudo-Maximum Likelihood Estimators and Groups of Transformations (2018)
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