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Updating Input-Output Tables with Benchmark Table Series

Author

Listed:
  • Huiwen Wang
  • Cheng Wang
  • Haitao Zheng
  • Haoyun Feng
  • Rong Guan
  • Wen Long

Abstract

Numerous methods have been proposed to update input-output (I-O) tables. They rely on the assumption that the economic structure will not change significantly during the interpolation period. However, this assumption may not always hold, particularly for countries experiencing rapid development. This study attempts to combine forecasting with a matrix transformation technique (MTT) to provide a new perspective on updating I-O tables. Under the assumption that changes in the trend of an economic structure are statistically significant, the method extrapolates I-O tables by combining time series models with an MTT and proceeds with only the total value added during the target years. A simulation study and empirical analysis are conducted to compare the forecasting performance of the MTT to the Generalized RAS (GRAS) and Kuroda methods. The results show that the comprehensive performance of the MTT is better than the performance of the GRAS and Kuroda methods, as measured by the Standardized Total Percentage Error, Theil's U and Mean Absolute Percentage Error indices.

Suggested Citation

  • Huiwen Wang & Cheng Wang & Haitao Zheng & Haoyun Feng & Rong Guan & Wen Long, 2015. "Updating Input-Output Tables with Benchmark Table Series," Economic Systems Research, Taylor & Francis Journals, vol. 27(3), pages 287-305, September.
    • Handle: RePEc:taf:ecsysr:v:27:y:2015:i:3:p:287-305
    DOI: 10.1080/09535314.2015.1053846
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    Cited by:

    1. Zheng, Haitao & Fang, Qi & Wang, Cheng & Jiang, Yunyun & Ren, Ruoen, 2018. "Updating China's input-output tables series using MTT method and its comparison," Economic Modelling, Elsevier, vol. 74(C), pages 186-193.
    2. Rong Guan & Haitao Zheng & Jie Hu & Qi Fang & Ruoen Ren, 2017. "The Higher Carbon Intensity of Loans, the Higher Non-Performing Loan Ratio: The Case of China," Sustainability, MDPI, vol. 9(4), pages 1-17, April.
    3. Jaime Nieto & Pedro B. Moyano & Diego Moyano & Luis Javier Miguel, 2023. "Is energy intensity a driver of structural change? Empirical evidence from the global economy," Journal of Industrial Ecology, Yale University, vol. 27(1), pages 283-296, February.
    4. Hiramatsu, Tomoru & Inoue, Hiroki & Kato, Yasuhiko, 2016. "Estimation of interregional input–output table using hybrid algorithm of the RAS method and real-coded genetic algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 95(C), pages 385-402.

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