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Semiparametric spatio-temporal models with unknown and banded autoregressive coefficient matrices
  • Hongxia Wang,
  • Xuehong Luo,
  • Long Ling
Hongxia Wang
Nanjing Audit University
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Xuehong Luo
Nanjing Audit University
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Long Ling
Nanjing Normal University
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Abstract

We consider a new class of semiparametric spatio-temporal models with unknown and banded autoregressive coefficient matrices. The setting represents a type of sparse structure in order to include as many panels as possible. We apply the local linear method and least squares method for Yule-Walker equation to estimate trend function and spatio-temporal autoregressive coefficient matrices respectively. We also balance the over-determined and under-determined phenomena in part by adjusting the order of extracting sample information. Both the asymptotic normality and convergence rates of the proposed estimators are established. The proposed methods are further illustrated using both simulation and case studies, the results also show that our estimator is stable among different sample size, and it performs better than the traditional method with known spatial weight matrices.

Peer review status:UNDER REVIEW

18 May 2021Submitted to Mathematical Methods in the Applied Sciences
19 May 2021Assigned to Editor
19 May 2021Submission Checks Completed
28 May 2021Reviewer(s) Assigned