Abstract
Input-Output analysis describes the dependence of production, demand and trade between sectors and regions and allows to understand the propagation of economic shocks through economic networks. A central challenge in practical applications is the availability of data. Observations may be limited to the impact of the shocks in few sectors, but a complete picture of the origin and impacts would be highly desirable to guide political countermeasures. In this article we demonstrate that a shock in the final demand in few sectors can be fully reconstructed from limited observations of production changes. We adapt three algorithms from sparse signal recovery and evaluate their performance and their robustness to observation uncertainties.
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References
Candès, E.J., Romberg, J.K., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59, 1207–1223 (2006)
Davis, G., Mallat, S., Avellaneda, M.: Adaptive greedy approximations. Constr. Approx. 13, 57–98 (1997)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52, 1289–1306 (2006)
Hallegatte, S.: An adaptive regional input-output model and its application to the assessment of the economic cost of Katrina. Risk Anal.: Int. J. 28, 779–799 (2008)
Marques, E.C., Maciel, N., Naviner, L., Cai, H., Yang, J.: A review of sparse recovery algorithms. IEEE Access 7, 1300–1322 (2018)
Miller, R.E., Blair, P.D.: Input-Output Analysis - Foundations and Extensions. Cambridge University Press, Cambridge (2009)
Needell, D., Tropp, J.: CoSaMP: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal. 26, 301–321 (2009)
Okuyama, Y., Santos, J.R.: Disaster impact and input-output analysis. Econ. Syst. Res. 26, 1–12 (2014)
Oosterhaven, J., Többen, J.: Wider economic impacts of heavy flooding in germany: a non-linear programming approach. Spat. Econ. Anal. 12, 404–428 (2017)
Pati, Y.C., Rezaiifar, R., Krishnaprasad, P.S.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, pp. 40–44 (1993)
Timme, M., Casadiego, J.: Revealing networks from dynamics: an introduction. J. Phys. A: Math. Theor. 47(34), 343001 (2014)
Timmer, M.P., Dietzenbacher, E., Los, B., Stehrer, R., Vries, G.J.: An illustrated user guide to the world input-output database: the case of global automotive production. Rev. Int. Econ. 23, 575–605 (2015)
Tropp, J.A., Wright, S.J.: Computational methods for sparse solution of linear inverse problems. Proc. IEEE 98, 948–958 (2010)
Acknowledgments
We gratefully acknowledge support from the Helmholtz association (grant no. VH-NG-1025), the German Ministry for Education and Research (BMBF grant no. 03SF0472) and the German Research Foundation (DFG) through the Cluster of Excellence Center for Advancing Electronics Dresden (cfaed) and the project ‘Bilinear Compressed Sensing’.
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Han, C., Többen, J., Kuckshinrichs, W., Schröder, M., Witthaut, D. (2020). Reconstruction of Demand Shocks in Input-Output Networks. In: Barbosa, H., Gomez-Gardenes, J., Gonçalves, B., Mangioni, G., Menezes, R., Oliveira, M. (eds) Complex Networks XI. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-40943-2_12
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