DIRECTIONALLY DIFFERENTIABLE ECONOMETRIC MODELS

Jin Seo Cho, Halbert White

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The current article examines the limit distribution of the quasi-maximum likelihood estimator obtained from a directionally differentiable quasi-likelihood function and represents its limit distribution as a functional of a Gaussian stochastic process indexed by direction. In this way, the standard analysis that assumes a differentiable quasi-likelihood function is treated as a special case of our analysis. We also examine and redefine the standard quasi-likelihood ratio, Wald, and Lagrange multiplier test statistics so that their null limit behaviors are regular under our model framework.

Original language English 1101-1131 31 Econometric Theory 34 5 https://doi.org/10.1017/S0266466617000354 Published - 2018 Oct 1

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econometrics
multiplier
statistics
Econometric models
Quasi-likelihood
Limit distribution
Likelihood ratio
Lagrange multiplier test
Quasi-maximum likelihood estimator
Test statistic
Stochastic processes

All Science Journal Classification (ASJC) codes

• Social Sciences (miscellaneous)
• Economics and Econometrics

Cite this

Cho, Jin Seo ; White, Halbert. / DIRECTIONALLY DIFFERENTIABLE ECONOMETRIC MODELS. In: Econometric Theory. 2018 ; Vol. 34, No. 5. pp. 1101-1131.
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DIRECTIONALLY DIFFERENTIABLE ECONOMETRIC MODELS. / Cho, Jin Seo; White, Halbert.

In: Econometric Theory, Vol. 34, No. 5, 01.10.2018, p. 1101-1131.

Research output: Contribution to journalArticle

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AB - The current article examines the limit distribution of the quasi-maximum likelihood estimator obtained from a directionally differentiable quasi-likelihood function and represents its limit distribution as a functional of a Gaussian stochastic process indexed by direction. In this way, the standard analysis that assumes a differentiable quasi-likelihood function is treated as a special case of our analysis. We also examine and redefine the standard quasi-likelihood ratio, Wald, and Lagrange multiplier test statistics so that their null limit behaviors are regular under our model framework.

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