Consistent Pseudo-Maximum Likelihood Estimators and Groups of Transformations

• 成果类型: Article
• 署名作者: Gourieroux, C.; Monfort, A.; Zakoian, J. -M.
• 署名单位: University of Toronto; Institut Polytechnique de Paris; ENSAE Paris; Universite de Lille
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA14727
• 发表日期: 2019
• 页码: 327-345
• 关键词: INDEPENDENT COMPONENT ANALYSIS; GARCH MODELS

摘要

In a transformation model yt=c[a(xt,beta),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 beta 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 beta. The usefulness of the consistency result is illustrated by examples of systems of nonlinear equations, conditionally heteroscedastic models, stochastic volatility, or models with spatial interactions.