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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.18773 |
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| _version_ | 1866915278873427968 |
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| author | Cen, Zetai Chen, Yudong Lam, Clifford |
| author_facet | Cen, Zetai Chen, Yudong Lam, Clifford |
| contents | We analyze a varying-coefficient dynamic spatial autoregressive model with spatial fixed effects. One salient feature of the model is the incorporation of multiple spatial weight matrices through their linear combinations with varying coefficients, which help solve the problem of choosing the most ``correct'' one for applied econometricians who often face the availability of multiple expert spatial weight matrices. We estimate and make inferences on the model coefficients and coefficients in basis expansions of the varying coefficients through penalized estimations, establishing the oracle properties of the estimators and the consistency of the overall estimated spatial weight matrix, which can be time-dependent. We further consider two applications of our model in change point detections in dynamic spatial autoregressive models, providing theoretical justifications in consistent change point locations estimation and practical implementations. Simulation experiments demonstrate the performance of our proposed methodology, and real data analyses are also carried out. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18773 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Inference on Dynamic Spatial Autoregressive Models with Change Point Detection Cen, Zetai Chen, Yudong Lam, Clifford Methodology Statistics Theory 62F30 We analyze a varying-coefficient dynamic spatial autoregressive model with spatial fixed effects. One salient feature of the model is the incorporation of multiple spatial weight matrices through their linear combinations with varying coefficients, which help solve the problem of choosing the most ``correct'' one for applied econometricians who often face the availability of multiple expert spatial weight matrices. We estimate and make inferences on the model coefficients and coefficients in basis expansions of the varying coefficients through penalized estimations, establishing the oracle properties of the estimators and the consistency of the overall estimated spatial weight matrix, which can be time-dependent. We further consider two applications of our model in change point detections in dynamic spatial autoregressive models, providing theoretical justifications in consistent change point locations estimation and practical implementations. Simulation experiments demonstrate the performance of our proposed methodology, and real data analyses are also carried out. |
| title | Inference on Dynamic Spatial Autoregressive Models with Change Point Detection |
| topic | Methodology Statistics Theory 62F30 |
| url | https://arxiv.org/abs/2411.18773 |