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Trending Time-Varying Coefficient Spatial Panel Data Models

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  • Hsuan-Yu Chang
  • Xiaojun Song
  • Jihai Yu

Abstract

This article investigates the estimation and inference of spatial panel data models in which the regression coefficient vector is a trending function. We use time differences to eliminate the individual effects and employ various GMM estimations for regression coefficients with both linear and quadratic moments. Time trend estimator based on these GMM estimations is also proposed. Monte Carlo experiments show that the finite sample performance of the estimators is satisfactory. As an empirical illustration, we investigate the trending pattern of the spillover effect of air pollution among Chinese cities from 2015 to 2021.

Suggested Citation

  • Hsuan-Yu Chang & Xiaojun Song & Jihai Yu, 2025. "Trending Time-Varying Coefficient Spatial Panel Data Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 43(1), pages 191-203, January.
    • Handle: RePEc:taf:jnlbes:v:43:y:2025:i:1:p:191-203
    DOI: 10.1080/07350015.2024.2340516
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