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The Knowledge Analysis of Panel Vector Autoregression: A Systematic Review

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  • Rui Yang
  • Xin An
  • Yingwen Chen
  • Xiuli Yang

Abstract

The panel vector autoregression (PVAR) model preserves the advantages of the vector autoregression model while expanding its time series to the spatial direction, which can effectively solve the problem of individual heterogeneity using panel data. It is derived from econometrics but has been applied interdisciplinarily because of its advantages in metrology. Given its increasingly important role in econometrics and interdisciplinary applications, a systematic review based on the bibliometric tool was conducted by screening 292 articles related to PVAR from the Web of Science. First, a descriptive analysis of the related articles was conducted to identify the current research status of PVAR. It reveals that macroeconomic effects, economic growth and environmental protection, and model adaptation are the primary topics in PVAR-related research. Then, the study classifies PVAR models into three categories and summarizes the four estimation methods within the knowledge domain. Having clarity on the different categories and estimation methods enhances the practical utility of the PVAR model. Finally, to gain insight into the knowledge evolution of PVAR, this study discusses how research hotspots in the field have evolved over time. This analysis provides a historical perspective and allows researchers to anticipate future trends and emerging areas of interest within PVAR. Based on these findings, this study identifies three research opportunities that can guide future investigations in the field of PVAR. This study aims to foster extension applications of the model in econometric research and highlight its potential for interdisciplinary applications.

Suggested Citation

  • Rui Yang & Xin An & Yingwen Chen & Xiuli Yang, 2023. "The Knowledge Analysis of Panel Vector Autoregression: A Systematic Review," SAGE Open, , vol. 13(4), pages 21582440231, December.
    • Handle: RePEc:sae:sagope:v:13:y:2023:i:4:p:21582440231215991
    DOI: 10.1177/21582440231215991
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