Inference on dynamic spatial autoregressive models with change point detection

Cen, Zetai, Chen, Yudong ORCID: 0000-0002-3034-4651 and Lam, Clifford ORCID: 0000-0001-8972-9129 (2025) Inference on dynamic spatial autoregressive models with change point detection. Journal of Business and Economic Statistics. ISSN 0735-0015 (In Press)

Text (spatial_ar_change) - Accepted Version Pending embargo until 1 January 2100. Available under License Creative Commons Attribution. Download (489kB)

Abstract

We analyze a varying-coefficient 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 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.

Item Type:	Article
Additional Information:	© 2025 The Author(s)
Divisions:	Statistics
Date Deposited:	24 Sep 2025 08:48
Last Modified:	24 Sep 2025 10:57
URI:	http://eprints.lse.ac.uk/id/eprint/129586

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