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Using a linear regression approach to sequential interindustry model for time-lagged economic impact analysis

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  • He, Kehan
  • Mi, Zhifu
  • Coffman, D'Maris
  • Guan, Dabo

Abstract

The input-output (IO) model is a powerful economic tool with many extended applications. However, one of the widely criticized drawbacks is its rather lengthy time lag in data preparation, making it impossible to apply IO in high-resolution time-series analysis. The conventional IO model is thus unfortunately unsuited for time-series analysis. In this study, we present an innovative algorithm that integrates linear regression techniques into a derivative of the IO method, the Sequential Interindustry Model (SIM), to overcome the inherent shortcomings of statistical lags in conventional IO studies. The regressed relationship can thus be used to predict, in the short term, the accumulated chronological impacts induced by fluctuations in sectorial economic demands under disequilibrium conditions. A simulated calculation is presented to serve as an illustration and verification of the new method. In the future, this application can be extended beyond economic studies to broader problems of system analysis.

Suggested Citation

  • He, Kehan & Mi, Zhifu & Coffman, D'Maris & Guan, Dabo, 2022. "Using a linear regression approach to sequential interindustry model for time-lagged economic impact analysis," Structural Change and Economic Dynamics, Elsevier, vol. 62(C), pages 399-406.
    • Handle: RePEc:eee:streco:v:62:y:2022:i:c:p:399-406
    DOI: 10.1016/j.strueco.2022.03.017
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    1. Chen, Ning & Li, Shaofang & Lu, Shuai, 2023. "The extreme risk connectedness of the global financial system: G7 and BRICS evidence," Journal of Multinational Financial Management, Elsevier, vol. 69(C).

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