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Estimation Errors in Input-Output Tables and Prediction Errors in Computable General Equilibrium Analysis

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  • Nobuhiro Hosoe

    (National Graduate Institute for Policy Studies)

Abstract

We used 1995-2000-2005 linked input-output (IO) tables for Japan to examine estimation errors of updated IO tables and the resulting prediction errors in computable general equilibrium (CGE) analysis developed with updated IO tables. As we usually have no true IO tables for the target year and therefore need to estimate them, we cannot evaluate estimation errors of updated IO tables without comparing the updated ones with true ones. However, using the linked IO tables covering three different years enables us to make this comparison. Our experiments showed that IO tables estimated with more detailed and recent data contained smaller estimation errors and led to smaller quantitative prediction errors in CGE analysis. Despite the quantitative prediction errors, prediction was found to be qualitatively correct. As for the performance of updating techniques of IO tables, a cross-entropy method often outperformed a least-squares method in IO estimation with only aggregate data for the target year but did not necessarily outperform the least-squares method in CGE prediction.

Suggested Citation

  • Nobuhiro Hosoe, 2013. "Estimation Errors in Input-Output Tables and Prediction Errors in Computable General Equilibrium Analysis," GRIPS Discussion Papers 13-16, National Graduate Institute for Policy Studies.
    • Handle: RePEc:ngi:dpaper:13-16
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    Cited by:

    1. Lin, Boqiang & Jia, Zhijie, 2018. "The energy, environmental and economic impacts of carbon tax rate and taxation industry: A CGE based study in China," Energy, Elsevier, vol. 159(C), pages 558-568.
    2. Wenlang Zhang & Gaofeng Han & Steven Chan, 2014. "How Strong are the Linkages between Real Estate and Other Sectors in China?," Working Papers 112014, Hong Kong Institute for Monetary Research.
    3. Lin, Boqiang & Jia, Zhijie, 2019. "Impacts of carbon price level in carbon emission trading market," Applied Energy, Elsevier, vol. 239(C), pages 157-170.
    4. Zheng, Xiaogui & Huang, Guohe & Liu, Lirong & Zhang, Xiaoyue, 2025. "Factorial equilibrium analyses for socio-economic implications of emission trading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 214(C).
    5. Lin, Boqiang & Jia, Zhijie, 2019. "What will China's carbon emission trading market affect with only electricity sector involvement? A CGE based study," Energy Economics, Elsevier, vol. 78(C), pages 301-311.
    6. Shogo Fukui, 2025. "Estimating Input Coefficients for Regional Input–Output Tables Using Deep Learning with Mixup," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 2423-2448, April.
    7. Anthony T. Flegg & Guiseppe R. Lamonica & Francesco M. Chelli & Maria C. Recchioni & Timo Tohmo, 2021. "A new approach to modelling the input–output structure of regional economies using non-survey methods," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 10(1), pages 1-31, December.

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