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Measuring US sectoral shocks in the world input–output network

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  • Ando, Sakai

Abstract

I measure the importance of sectoral shocks in US aggregate output by using the World Input–Output Table (WIOT). The WIOT allows me to correct potential sub-graph bias in previous literature, caused by using only the US industrial production input–output table. I report results from three closely related models to show how sensitive the analyses are to different specifications. The estimates vary from 10% to 45%.

Suggested Citation

  • Ando, Sakai, 2014. "Measuring US sectoral shocks in the world input–output network," Economics Letters, Elsevier, vol. 125(2), pages 204-207.
    • Handle: RePEc:eee:ecolet:v:125:y:2014:i:2:p:204-207
    DOI: 10.1016/j.econlet.2014.09.007
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    References listed on IDEAS

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    1. Vasco Carvalho, 2007. "Aggregate fluctuations and the network structure of intersectoral trade," Economics Working Papers 1206, Department of Economics and Business, Universitat Pompeu Fabra, revised Oct 2010.
    2. Andrew T. Foerster & Pierre-Daniel G. Sarte & Mark W. Watson, 2011. "Sectoral versus Aggregate Shocks: A Structural Factor Analysis of Industrial Production," Journal of Political Economy, University of Chicago Press, vol. 119(1), pages 1-38.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Shea, John S, 2002. "Complementarities and Comovements," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(2), pages 412-433, May.
    5. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
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    Citations

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    Cited by:

    1. Dongyeol Lee, 2019. "Transmission of Domestic and External Shocks through Input-Output Network: Evidence from Korean Industries," IMF Working Papers 2019/117, International Monetary Fund.
    2. Xing, Lizhi & Dong, Xianlei & Guan, Jun, 2017. "Global industrial impact coefficient based on random walk process and inter-country input–output table," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 576-591.
    3. Kim, Daisoon, 2021. "Economies of scale and international business cycles," Journal of International Economics, Elsevier, vol. 131(C).
    4. Dungey, Mardi & Volkov, Vladimir, 2018. "R&D and wholesale trade are critical to the economy: Identifying dominant sectors from economic networks," Economics Letters, Elsevier, vol. 162(C), pages 81-85.
    5. Dongyeol Lee, 2019. "Trade Linkages and International Business Cycle Comovement: Evidence from Korean Industry Data," IMF Working Papers 2019/116, International Monetary Fund.
    6. Pedro P Romero & Ricardo López & Carlos Jiménez, 2018. "Sectoral networks and macroeconomic tail risks in an emerging economy," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-17, January.
    7. Xing, Lizhi & Guan, Jun & Dong, Xianlei & Wu, Shan, 2018. "Understanding the competitive advantage of TPP-related nations from an econophysics perspective: Influence caused by China and the United States," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 164-184.
    8. Dong, Feng & Wen, Yi, 2019. "Long and Plosser meet Bewley and Lucas," Journal of Monetary Economics, Elsevier, vol. 102(C), pages 70-92.
    9. Weidong Li & Anjian Wang & Weiqiong Zhong & Chunhui Wang, 2022. "An Impact Path Analysis of Russo–Ukrainian Conflict on the World and Policy Response Based on the Input–Output Network," Sustainability, MDPI, vol. 14(14), pages 1-17, July.
    10. Li, Nan & Martin, Vance L., 2019. "Real sectoral spillovers: A dynamic factor analysis of the great recession," Journal of Monetary Economics, Elsevier, vol. 107(C), pages 77-95.
    11. Tsekeris, Theodore, 2017. "Global value chains: Building blocks and network dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 488(C), pages 187-204.
    12. Xing, Lizhi & Guan, Jun & Wu, Shan, 2018. "Measuring the impact of final demand on global production system based on Markov process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 148-163.
    13. Feng Dong & Yi Wen, 2019. "Time-Varying Networks and the Efficacy of Money Without Sticky Prices," 2019 Meeting Papers 1464, Society for Economic Dynamics.
    14. Xing, Lizhi & Wang, Dawei & Li, Yan & Guan, Jun & Dong, Xianlei, 2020. "Simulation analysis of the competitive status between China and Portuguese-speaking countries under the background of one belt and one road initiative," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).

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    More about this item

    Keywords

    Sectoral shock; Aggregate fluctuation; Production network; Factor analysis;
    All these keywords.

    JEL classification:

    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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