IDEAS home Printed from https://ideas.repec.org/a/wly/quante/v14y2023i3p1021-1058.html
   My bibliography  Save this article

Estimating large‐dimensional connectedness tables: The great moderation through the lens of sectoral spillovers

Author

Listed:
  • Felix Brunner
  • Ruben Hipp

Abstract

We estimate sectoral spillovers around the Great Moderation with the help of forecast error variance decomposition tables. Obtaining such tables in high dimensions is challenging because they are functions of the estimated vector autoregressive coefficients and the residual covariance matrix. In a simulation study, we compare various regularization methods on both and conduct a comprehensive analysis of their performance. We show that standard estimators of large connectedness tables lead to biased results and high estimation uncertainty, both of which are mitigated by regularization. To explore possible causes for the Great Moderation, we apply a cross‐validated estimator on sectoral spillovers of industrial production in the US from 1972 to 2019. We find that the spillover network has considerably weakened, which hints at structural change, for example, through improved inventory management, as a critical explanation for the Great Moderation.

Suggested Citation

  • Felix Brunner & Ruben Hipp, 2023. "Estimating large‐dimensional connectedness tables: The great moderation through the lens of sectoral spillovers," Quantitative Economics, Econometric Society, vol. 14(3), pages 1021-1058, July.
    • Handle: RePEc:wly:quante:v:14:y:2023:i:3:p:1021-1058
    DOI: 10.3982/QE1947
    as

    Download full text from publisher

    File URL: https://doi.org/10.3982/QE1947
    Download Restriction: no
    File URL: https://libkey.io/10.3982/QE1947?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    3. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
    4. Vasco M. Carvalho, 2014. "From Micro to Macro via Production Networks," Journal of Economic Perspectives, American Economic Association, vol. 28(4), pages 23-48, Fall.
    5. Steven J. Davis & James A. Kahn, 2008. "Interpreting the Great Moderation: Changes in the Volatility of Economic Activity at the Macro and Micro Levels," Journal of Economic Perspectives, American Economic Association, vol. 22(4), pages 155-180, Fall.
    6. Luca Gambetti & Jordi Galí, 2009. "On the Sources of the Great Moderation," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(1), pages 26-57, January.
    7. Vasco Carvalho & Xavier Gabaix, 2013. "The Great Diversification and Its Undoing," American Economic Review, American Economic Association, vol. 103(5), pages 1697-1727, August.
    8. Roberto Rigobon & Brian Sack, 2003. "Measuring The Reaction of Monetary Policy to the Stock Market," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(2), pages 639-669.
    9. Andres Arias & Gary Hansen & Lee Ohanian, 2007. "Why have business cycle fluctuations become less volatile?," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 32(1), pages 43-58, July.
    10. Andrew T. Foerster & Andreas Hornstein & Pierre-Daniel G. Sarte & Mark W. Watson, 2022. "Aggregate Implications of Changing Sectoral Trends," Journal of Political Economy, University of Chicago Press, vol. 130(12), pages 3286-3333.
    11. Kascha, Christian & Trenkler, Carsten, 2015. "Forecasting VARs, model selection, and shrinkage," Working Papers 15-07, University of Mannheim, Department of Economics.
    12. Matteo Barigozzi & Christian Brownlees, 2019. "NETS: Network estimation for time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(3), pages 347-364, April.
    13. Xavier Gabaix, 2011. "The Granular Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 79(3), pages 733-772, May.
    14. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    15. Anton Nakov & Andrea Pescatori, 2010. "Oil and the Great Moderation," Economic Journal, Royal Economic Society, vol. 120(543), pages 131-156, March.
    16. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    17. Christina D. Romer & David H. Romer, 2004. "A New Measure of Monetary Shocks: Derivation and Implications," American Economic Review, American Economic Association, vol. 94(4), pages 1055-1084, September.
    18. Cai, Tony & Liu, Weidong, 2011. "Adaptive Thresholding for Sparse Covariance Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 672-684.
    19. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    20. Sylvain Leduc & Keith Sill, 2007. "Monetary Policy, Oil Shocks, and TFP: Accounting for the Decline in U.S. Volatility," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 10(4), pages 595-614, October.
    21. Ruben Hipp, 2020. "On Causal Networks of Financial Firms: Structural Identification via Non-parametric Heteroskedasticity," Staff Working Papers 20-42, Bank of Canada.
    22. Rothman, Adam J. & Levina, Elizaveta & Zhu, Ji, 2009. "Generalized Thresholding of Large Covariance Matrices," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 177-186.
    23. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    24. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    25. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
    26. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    27. Robert L. Bray & Haim Mendelson, 2012. "Information Transmission and the Bullwhip Effect: An Empirical Investigation," Management Science, INFORMS, vol. 58(5), pages 860-875, May.
    28. James A. Kahn & Margaret M. McConnell & Gabriel Perez-Quiros, 2002. "On the causes of the increased stability of the U.S. economy," Economic Policy Review, Federal Reserve Bank of New York, vol. 8(May), pages 183-202.
    29. Hong Chen & Murray Z. Frank & Owen Q. Wu, 2005. "What Actually Happened to the Inventories of American Companies Between 1981 and 2000?," Management Science, INFORMS, vol. 51(7), pages 1015-1031, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Matteo Barigozzi & Marc Hallin, 2015. "Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series," Working Papers ECARES ECARES 2015-34, ULB -- Universite Libre de Bruxelles.
    2. Miao, Ke & Phillips, Peter C.B. & Su, Liangjun, 2023. "High-dimensional VARs with common factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 155-183.
    3. Felix Brunner & Ruben Hipp, 2021. "Estimating Large-Dimensional Connectedness Tables: The Great Moderation Through the Lens of Sectoral Spillovers," Staff Working Papers 21-37, Bank of Canada.
    4. Matteo Barigozzi & Marc Hallin, 2017. "A network analysis of the volatility of high dimensional financial series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 581-605, April.
    5. Laura Liu & Christian Matthes & Katerina Petrova, 2022. "Monetary Policy Across Space and Time," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 37-64, Emerald Group Publishing Limited.
    6. Krampe, J. & Paparoditis, E. & Trenkler, C., 2023. "Structural inference in sparse high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 234(1), pages 276-300.
    7. Linh Xuan Diep Nguyen & Thanaset Chevapatrakul & Simona Mateut, 2023. "Shock transmissions and business linkages among US sectors," Annals of Operations Research, Springer, vol. 330(1), pages 517-552, November.
    8. Ge, S., 2020. "Text-Based Linkages and Local Risk Spillovers in the Equity Market," Cambridge Working Papers in Economics 20115, Faculty of Economics, University of Cambridge.
    9. Paflioti, Persa & Vitsounis, Thomas K. & Teye, Collins & Bell, Michael G.H. & Tsamourgelis, Ioannis, 2017. "Box dynamics: A sectoral approach to analyse containerized port throughput interdependencies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 396-413.
    10. Yu Chen & Jie Hu & Weiping Zhang, 2020. "Too Connected to Fail? Evidence from a Chinese Financial Risk Spillover Network," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 28(6), pages 78-100, November.
    11. Everett Grant & Julieta Yung, 2021. "The double‐edged sword of global integration: Robustness, fragility, and contagion in the international firm network," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 760-783, September.
    12. Shi Chen & Wolfgang Karl Hardle & Brenda L'opez Cabrera, 2020. "Regularization Approach for Network Modeling of German Power Derivative Market," Papers 2009.09739, arXiv.org.
    13. Kamil Yilmaz, 2018. "Bank Volatility Connectedness in South East Asia," Koç University-TUSIAD Economic Research Forum Working Papers 1807, Koc University-TUSIAD Economic Research Forum.
    14. Okorie, David Iheke & Lin, Boqiang, 2022. "Givers never lack: Nigerian oil & gas asymmetric network analyses," Energy Economics, Elsevier, vol. 108(C).
    15. Muñoz Mendoza, Jorge A. & Veloso Ramos, Carmen L. & Delgado Fuentealba, Carlos L. & Cornejo Saavedra, Edinson E. & Sepúlveda Yelpo, Sandra M., 2024. "Stock, foreign exchange and commodity markets linkages: Implications for risk diversification and portfolio management," Global Finance Journal, Elsevier, vol. 63(C).
    16. Chen, Shi & Härdle, Wolfgang Karl & López Cabrera, Brenda, 2018. "Regularization Approach for Network Modeling of German Energy Market," IRTG 1792 Discussion Papers 2018-017, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    17. Thomas F. P. Wiesen & Paul M. Beaumont, 2024. "A joint impulse response function for vector autoregressive models," Empirical Economics, Springer, vol. 66(4), pages 1553-1585, April.
    18. Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Estimating global bank network connectedness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(1), pages 1-15, January.
    19. Bastien Buchwalter & Francis X. Diebold & Kamil Yilmaz, 2025. "Clustered Network Connectedness:A New Measurement Frameworkwith Application to Global Equity Markets," PIER Working Paper Archive 25-009, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    20. Balcilar, Mehmet & Ozdemir, Huseyin & Agan, Busra, 2022. "Effects of COVID-19 on cryptocurrency and emerging market connectedness: Empirical evidence from quantile, frequency, and lasso networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:quante:v:14:y:2023:i:3:p:1021-1058. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/essssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.