IDEAS home Printed from https://ideas.repec.org/p/koc/wpaper/1813.html
   My bibliography  Save this paper

Producer Price Inflation Connectedness and Input-Output Networks

Author

Listed:
  • N. Melisa Bilgin

    () (Koç University)

  • Kamil Yilmaz

    () (Koç University)

Abstract

We analyze the transmission of producer price in inflation shocks across the U.S. manufacturing industries from 1947 to 2018 using the Diebold-Yilmaz Connectedness Index framework, which fully utilizes the information in generalized variance decompositions from vector autoregressions. The results show that the system-wide connectedness of the input-output network Granger-causes the producer price inflation connectedness across industries. The input-output network and the inflation connectedness nexus is stronger during periods of major supply-side shocks, such as the global oil and metal price hikes, and weaker during periods of aggregate demand shocks, such as the Volcker disinflation of 1981-84 and the Great Recession of 2008. These findings are consistent with Acemoglu et al. (2016)'s conjecture that supply shocks are transmitted downstream, whereas demand shocks are transmitted upstream. Finally, preliminary results show that Trump tariffs caused an increase in the system-wide inflation connectedness in the first half of 2018, due to shocks mostly transmitted from tariff-targeted industries, namely, basic metals, fabricated metals and machinery.

Suggested Citation

  • N. Melisa Bilgin & Kamil Yilmaz, 2018. "Producer Price Inflation Connectedness and Input-Output Networks," Koç University-TUSIAD Economic Research Forum Working Papers 1813, Koc University-TUSIAD Economic Research Forum.
  • Handle: RePEc:koc:wpaper:1813
    as

    Download full text from publisher

    File URL: http://eaf.ku.edu.tr/sites/eaf.ku.edu.tr/files/erf_wp_1813.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Pengfei & Wen, Yi, 2007. "Inflation dynamics: A cross-country investigation," Journal of Monetary Economics, Elsevier, vol. 54(7), pages 2004-2031, October.
    2. Raphael A. Auer & Andrei A. Levchenko & Philip Sauré, 2019. "International Inflation Spillovers through Input Linkages," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 507-521, July.
    3. FrancisX. Diebold & Kamil Yilmaz, 2009. "Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets," Economic Journal, Royal Economic Society, vol. 119(534), pages 158-171, January.
    4. Enghin Atalay, 2017. "How Important Are Sectoral Shocks?," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(4), pages 254-280, October.
    5. 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.
    6. Haroon Mumtaz & Paolo Surico, 2009. "The Transmission of International Shocks: A Factor-Augmented VAR Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(s1), pages 71-100, February.
    7. Daron Acemoglu & Ufuk Akcigit & William Kerr, 2016. "Networks and the Macroeconomy: An Empirical Exploration," NBER Macroeconomics Annual, University of Chicago Press, vol. 30(1), pages 273-335.
    8. Tommaso Monacelli & Luca Sala, 2009. "The International Dimension of Inflation: Evidence from Disaggregated Consumer Price Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(s1), pages 101-120, February.
    9. Long, John B, Jr & Plosser, Charles I, 1983. "Real Business Cycles," Journal of Political Economy, University of Chicago Press, vol. 91(1), pages 39-69, February.
    10. David Rezza Baqaee & Emmanuel Farhi, 2019. "The Macroeconomic Impact of Microeconomic Shocks: Beyond Hulten's Theorem," Econometrica, Econometric Society, vol. 87(4), pages 1155-1203, July.
    11. 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.
    12. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    13. Radetzki, Marian, 2006. "The anatomy of three commodity booms," Resources Policy, Elsevier, vol. 31(1), pages 56-64, March.
    14. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    15. Raphael A Auer & Philip Sauré, 2013. "The globalisation of inflation: a view from the cross section," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 113-118, Bank for International Settlements.
    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. Jozef Barunik & Mattia Bevilacqua & Robert Faff, 2021. "Dynamic industry uncertainty networks and the business cycle," Papers 2101.06957, arXiv.org, revised Mar 2021.
    2. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
    3. Evrim Mandacı, Pınar & Cagli, Efe Çaglar & Taşkın, Dilvin, 2020. "Dynamic connectedness and portfolio strategies: Energy and metal markets," Resources Policy, Elsevier, vol. 68(C).
    4. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & Perez de Gracia, Fernando, 2018. "Oil volatility, oil and gas firms and portfolio diversification," Energy Economics, Elsevier, vol. 70(C), pages 499-515.
    5. Francis X. Diebold & Kamil Yilmaz, 2016. "Trans-Atlantic Equity Volatility Connectedness: U.S. and European Financial Institutions, 2004–2014," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(1), pages 81-127.
    6. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2017. "Oil shocks and stock markets: Dynamic connectedness under the prism of recent geopolitical and economic unrest," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 1-26.
    7. 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.
    8. Beyer, Andreas & Alter, Adrian, 2013. "The dynamics of spillover effects during the European sovereign debt crisis," Working Paper Series 1558, European Central Bank.
    9. Fasanya, Ismail & Akinbowale, Seun, 2019. "Modelling the return and volatility spillovers of crude oil and food prices in Nigeria," Energy, Elsevier, vol. 169(C), pages 186-205.
    10. Li, Yanfei & Ji, Qiang & Zhang, Dayong, 2020. "Technological catching up and innovation policies in China: What is behind this largely successful story?," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    11. Nikolaos Antonakakis & Ioannis Chatziantoniou & David Gabauer, 2020. "Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 13(4), pages 1-23, April.
    12. Chatziantoniou, Ioannis & Gabauer, David & Stenfors, Alexis, 2020. "From CIP-deviations to a market for risk premia: A dynamic investigation of cross-currency basis swaps," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 69(C).
    13. Vaclav Broz & Evzen Kocenda, 2019. "Mortgage-Related Bank Penalties and Systemic Risk Among U.S. Banks," Working Papers IES 2019/25, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2019.
    14. Zhang, Yulian & He, Xie & Nakajima, Tadahiro & Hamori, Shigeyuki, 2020. "Oil, Gas, or Financial Conditions-Which One Has a Stronger Link with Growth?," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    15. Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2018. "Commodity Connectedness," Central Banking, Analysis, and Economic Policies Book Series, in: Enrique G. Mendoza & Ernesto Pastén & Diego Saravia (ed.),Monetary Policy and Global Spillovers: Mechanisms, Effects and Policy Measures, edition 1, volume 25, chapter 4, pages 097-136, Central Bank of Chile.
    16. Fowowe, Babajide & Shuaibu, Mohammed, 2016. "Dynamic spillovers between Nigerian, South African and international equity markets," International Economics, Elsevier, vol. 148(C), pages 59-80.
    17. Magkonis, Georgios & Tsouknidis, Dimitris A., 2017. "Dynamic spillover effects across petroleum spot and futures volatilities, trading volume and open interest," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 104-118.
    18. Erdenebat Bataa & Andrew Vivian & Mark Wohar, 2019. "Changes in the relationship between short‐term interest rate, inflation and growth: evidence from the UK, 1820–2014," Bulletin of Economic Research, Wiley Blackwell, vol. 71(4), pages 616-640, October.
    19. Gabauer, David & Gupta, Rangan, 2020. "Spillovers across macroeconomic, financial and real estate uncertainties: A time-varying approach," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 167-173.
    20. Hsu, Chih-Hsiang & Lee, Hsiu-Chuan & Lien, Donald, 2020. "Stock market uncertainty, volatility connectedness of financial institutions, and stock-bond return correlations," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 600-621.

    More about this item

    Keywords

    Input-output networks; Inflation; Connectedness; Supply-side shocks; Commodity prices; Business cycles; Vector autoregression; Variance decomposition.;
    All these keywords.

    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • D57 - Microeconomics - - General Equilibrium and Disequilibrium - - - Input-Output Tables and Analysis
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:koc:wpaper:1813. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sumru Oz). General contact details of provider: https://edirc.repec.org/data/dekoctr.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.