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This is a Quantitative Study study.

September 29, 2025Open Access

On a new robust method of inference for general time series models

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Authors

ZWZihan WangNanjing University of Information Science and Technology XQXinghao QiaoUniversity of Hong Kong DLDong LiUniversität Hamburg

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Implication

This article proposes a new logistic quasi-maximum likelihood estimation for time series models, suggesting significant robustness and performance improvements.

Key Points

The logistic quasi-maximum likelihood estimation provides robustness against distributional misspecification and outliers.
Strong consistency and asymptotic normality of the LQMLE are established under mild conditions.
Wald and Lagrange multiplier tests for unknown parameters are developed based on the LQMLE.
Numerical simulations and data examples validate the finite-sample performance of the proposed methodology.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/68da58d1c1728099cfd10b94 https://doi.org/10.48550/arxiv.2503.08655

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Implication

This article proposes a new logistic quasi-maximum likelihood estimation for time series models, suggesting significant robustness and performance improvements.

On a new robust method of inference for general time series models

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Member takes

Implication

Key Points

Cite This Study

On a new robust method of inference for general time series models

Discussion

Member takes

Implication

Key Points

Cite This Study

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