Inference for non-stationary time series quantile regression with inequality constraints
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911828458602496 |
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| author | Sun, Yuan Zhou, Zhou |
| author_facet | Sun, Yuan Zhou, Zhou |
| contents | We consider parameter inference for linear quantile regression with non-stationary predictors and errors, where the regression parameters are subject to inequality constraints. We show that the constrained quantile coefficient estimators are asymptotically equivalent to the metric projections of the unconstrained estimator onto the constrained parameter space. Utilizing a geometry-invariant property of this projection operation, we propose inference procedures - the Wald, likelihood ratio, and rank-based methods - that are consistent regardless of whether the true parameters lie on the boundary of the constrained parameter space. We also illustrate the advantages of considering the inequality constraints in analyses through simulations and an application to an electricity demand dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03837 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Inference for non-stationary time series quantile regression with inequality constraints Sun, Yuan Zhou, Zhou Methodology 62M10, 62G08, 62F40 We consider parameter inference for linear quantile regression with non-stationary predictors and errors, where the regression parameters are subject to inequality constraints. We show that the constrained quantile coefficient estimators are asymptotically equivalent to the metric projections of the unconstrained estimator onto the constrained parameter space. Utilizing a geometry-invariant property of this projection operation, we propose inference procedures - the Wald, likelihood ratio, and rank-based methods - that are consistent regardless of whether the true parameters lie on the boundary of the constrained parameter space. We also illustrate the advantages of considering the inequality constraints in analyses through simulations and an application to an electricity demand dataset. |
| title | Inference for non-stationary time series quantile regression with inequality constraints |
| topic | Methodology 62M10, 62G08, 62F40 |
| url | https://arxiv.org/abs/2404.03837 |