An alternative quasi likelihood approach, Bayesian analysis and data-based inference for model specification

Jae-Young Kim

Journal of Econometrics, 2014, vol. 178, issue P1, 132-145

Abstract: This paper studies an alternative quasi likelihood approach under possible model misspecification. We derive a filtered likelihood from a given quasi likelihood (QL), called a limited information quasi likelihood (LI-QL), that contains relevant but limited information on the data generation process. Our LI-QL approach, in one hand, extends robustness of the QL approach to inference problems for which the existing approach does not apply. Our study in this paper, on the other hand, builds a bridge between the classical and Bayesian approaches for statistical inference under possible model misspecification. We can establish a large sample correspondence between the classical QL approach and our LI-QL based Bayesian approach. An interesting finding is that the asymptotic distribution of an LI-QL based posterior and that of the corresponding quasi maximum likelihood estimator share the same “sandwich”-type second moment. Based on the LI-QL we can develop inference methods that are useful for practical applications under possible model misspecification. In particular, we can develop the Bayesian counterparts of classical QL methods that carry all the nice features of the latter studied in White (1982). In addition, we can develop a Bayesian method for analyzing model specification based on an LI-QL.

Keywords: Quasi likelihood; Model misspecification; Limited information; Bayesian methods; Sandwich covariance; Large sample correspondence; Model selection (search for similar items in EconPapers)
JEL-codes: C11 C14 C2 C3 C5 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:178:y:2014:i:p1:p:132-145

DOI: 10.1016/j.jeconom.2013.08.012

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