Quasi-maximum likelihood estimation revisited using the distance and direction method

Jin Seo Cho

Quasi-maximum likelihood estimation revisited using the distance and direction method

Jin Seo Cho

Department of Economics

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

We examine an asymptotic analysis of differentiable econometric models using the distance and direction (DD) method introduced by Cho and White (2012), in which the conventional analysis for the quasi-maximum likelihood estimation and inference can be treated as a special case. We extend their approach and revisit the conventional quasi-likelihood ratio, Wald, and Lagrange multiplier test statistics through a different perspective. This new perspective is further analyzed in a unified framework, and we exploit this to introduce new classes of test statistics.

Original language	English
Pages (from-to)	89-112
Number of pages	24
Journal	Journal of Economic Theory and Econometrics
Volume	23
Issue number	2
Publication status	Published - 2012 Jun

All Science Journal Classification (ASJC) codes

Economics and Econometrics

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abstract = "We examine an asymptotic analysis of differentiable econometric models using the distance and direction (DD) method introduced by Cho and White (2012), in which the conventional analysis for the quasi-maximum likelihood estimation and inference can be treated as a special case. We extend their approach and revisit the conventional quasi-likelihood ratio, Wald, and Lagrange multiplier test statistics through a different perspective. This new perspective is further analyzed in a unified framework, and we exploit this to introduce new classes of test statistics.",

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