Statistical inference of partially linear time-varying coefficients spatial autoregressive panel data model
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917802118479872 |
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| author | Tian, Lingling Wei, Chuanhua Wu, Mixia |
| author_facet | Tian, Lingling Wei, Chuanhua Wu, Mixia |
| contents | This paper investigates a partially linear spatial autoregressive panel data model that incorporates fixed effects, constant and time-varying regression coefficients, and a time-varying spatial lag coefficient. A two-stage least squares estimation method based on profile local linear dummy variables (2SLS-PLLDV) is proposed to estimate both constant and time-varying coefficients without the need for first differencing. The asymptotic properties of the estimator are derived under certain conditions. Furthermore, a residual-based goodness-of-fit test is constructed for the model, and a residual-based bootstrap method is used to obtain p-values. Simulation studies show the good performance of the proposed method in various scenarios. The Chinese provincial carbon emission data set is analyzed for illustration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10647 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Statistical inference of partially linear time-varying coefficients spatial autoregressive panel data model Tian, Lingling Wei, Chuanhua Wu, Mixia Statistics Theory 62F40 G.3 This paper investigates a partially linear spatial autoregressive panel data model that incorporates fixed effects, constant and time-varying regression coefficients, and a time-varying spatial lag coefficient. A two-stage least squares estimation method based on profile local linear dummy variables (2SLS-PLLDV) is proposed to estimate both constant and time-varying coefficients without the need for first differencing. The asymptotic properties of the estimator are derived under certain conditions. Furthermore, a residual-based goodness-of-fit test is constructed for the model, and a residual-based bootstrap method is used to obtain p-values. Simulation studies show the good performance of the proposed method in various scenarios. The Chinese provincial carbon emission data set is analyzed for illustration. |
| title | Statistical inference of partially linear time-varying coefficients spatial autoregressive panel data model |
| topic | Statistics Theory 62F40 G.3 |
| url | https://arxiv.org/abs/2410.10647 |