IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/125580.html
   My bibliography  Save this paper

A Bayesian vector-autoregressive application with time-varying parameters on the monetary shocks-production network nexus

Author

Listed:
  • Simionescu, Mihaela
  • Schneider, Nicolas
  • Gavurova, Beata

Abstract

Transmission channels from monetary shocks might be identified by studying the features of the production network. The main aim of this paper is to provide insights about the role of production network into the propagation of monetary policy shocks in G7 economies. Time-varying Bayesian vector-autoregressions were built to compute impulse response functions of output to monetary policy shocks in these countries. Panel Auto-Regressive Distributed Lag Bound Approach based on Mean-Group estimator was used to assess the long and short-run connections between production network structure and various shocks associated to monetary policy in the period 2000–2018 and during the Great Recession (2007–2009). The results show that upstreamness is more significant than downstremness in the period 2000–2018, while the financial sector significantly contributed to the spread of various monetary shocks during the Great Recession.

Suggested Citation

  • Simionescu, Mihaela & Schneider, Nicolas & Gavurova, Beata, 2024. "A Bayesian vector-autoregressive application with time-varying parameters on the monetary shocks-production network nexus," LSE Research Online Documents on Economics 125580, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:125580
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/125580/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Cushman, David O. & Michael, Nils, 2011. "Nonlinear trends in real exchange rates: A panel unit root test approach," Journal of International Money and Finance, Elsevier, vol. 30(8), pages 1619-1637.
    3. Pesaran, M.H. & Shin, Y., 1995. "An Autoregressive Distributed Lag Modelling Approach to Cointegration Analysis," Cambridge Working Papers in Economics 9514, Faculty of Economics, University of Cambridge.
    4. Emi Nakamura & Jón Steinsson, 2010. "Monetary Non-neutrality in a Multisector Menu Cost Model," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(3), pages 961-1013.
    5. Daron Acemoglu & Asuman Ozdaglar & Alireza Tahbaz-Salehi, 2015. "Systemic Risk and Stability in Financial Networks," American Economic Review, American Economic Association, vol. 105(2), pages 564-608, February.
    6. Kapetanios, George & Shin, Yongcheol & Snell, Andy, 2003. "Testing for a unit root in the nonlinear STAR framework," Journal of Econometrics, Elsevier, vol. 112(2), pages 359-379, February.
    7. Daron Acemoglu & Vasco M. Carvalho & Asuman Ozdaglar & Alireza Tahbaz‐Salehi, 2012. "The Network Origins of Aggregate Fluctuations," Econometrica, Econometric Society, vol. 80(5), pages 1977-2016, September.
    8. Pasten, Ernesto & Schoenle, Raphael & Weber, Michael, 2020. "The propagation of monetary policy shocks in a heterogeneous production economy," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 1-22.
    9. Emmanuel Dhyne & Ayumu Ken Kikkawa & Magne Mogstad & Felix Tintelnot, 2021. "Trade and Domestic Production Networks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(2), pages 643-668.
    10. Carlos Carvalho & Jae Won Lee & Woong Yong Park, 2021. "Sectoral Price Facts in a Sticky-Price Model," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(1), pages 216-256, January.
    11. Jörg Breitung & Samarjit Das, 2005. "Panel unit root tests under cross‐sectional dependence," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 59(4), pages 414-433, November.
    12. Bartosz Mackowiak & Frank Smets, 2008. "On implications of micro price data for macro models," Conference Series ; [Proceedings], Federal Reserve Bank of Boston.
    13. Eugene Kouassi & Lexi L Setlhare, 2016. "Asymptotic Properties of Pesaran's CD Test Revisited," Economics Bulletin, AccessEcon, vol. 36(4), pages 2569-2578.
    14. Basile Grassi & Julien Sauvagnat, 2019. "Production networks and economic policy," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 35(4), pages 638-677.
    15. Liddle, Brantley, 2015. "What Are the Carbon Emissions Elasticities for Income and Population? Bridging STIRPAT and EKC via robust heterogeneous panel estimates," MPRA Paper 61304, University Library of Munich, Germany.
    16. repec:bla:obuest:v:61:y:1999:i:0:p:631-52 is not listed on IDEAS
    17. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    18. G. S. Maddala & Shaowen Wu, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 631-652, November.
    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. Ghassibe, Mishel, 2021. "Monetary policy and production networks: an empirical investigation," Journal of Monetary Economics, Elsevier, vol. 119(C), pages 21-39.
    2. Ryan Chahrour & Manoj Atolia, 2015. "Intersectoral Linkages, Diverse Information, and Aggregate Dynamics in a Neoclassical Model," 2015 Meeting Papers 398, Society for Economic Dynamics.
    3. Manoj Atolia & Ryan Chahrour, 2020. "Intersectoral Linkages, Diverse Information, and Aggregate Dynamics," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 36, pages 270-292, April.
    4. Frank Smets & Joris Tielens & Jan Van Hove, 2018. "Pipeline Pressures and Sectoral Inflation Dynamics," Working Paper Research 351, National Bank of Belgium.
    5. Vasco M. Carvalho & Alireza Tahbaz-Salehi, 2019. "Production Networks: A Primer," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 635-663, August.
    6. MIZUNO Takayuki & SOUMA Wataru & WATANABE Tsutomu, 2015. "Buyer-Supplier Networks and Aggregate Volatility," Discussion papers 15056, Research Institute of Economy, Trade and Industry (RIETI).
    7. 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.
    8. Hussein Moghaddam & Robert M. Kunst, 2023. "The Role of Natural Gas in Mitigating Greenhouse Gas Emissions: The Environmental Kuznets Curve Hypothesis for Major Gas-Producing Countries," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
    9. Huang, Yi & Lin, Chen & Liu, Sibo & Tang, Heiwai, 2023. "Trade networks and firm value: Evidence from the U.S.-China trade war," Journal of International Economics, Elsevier, vol. 145(C).
    10. Ernesto Pasten & Raphael S. Schoenle & Michael Weber & Michael Weber, 2017. "Price Rigidities and the Granular Origins of Aggregate Fluctuations," CESifo Working Paper Series 6619, CESifo.
    11. Yuzuka Kashiwagi & Yasuyuki Todo & Petr Matous, 2021. "Propagation of economic shocks through global supply chains—Evidence from Hurricane Sandy," Review of International Economics, Wiley Blackwell, vol. 29(5), pages 1186-1220, November.
    12. Bernard Herskovic & Bryan Kelly & Hanno Lustig & Stijn Van Nieuwerburgh, 2020. "Firm Volatility in Granular Networks," Journal of Political Economy, University of Chicago Press, vol. 128(11), pages 4097-4162.
    13. Baruník, Jozef & Ellington, Michael, 2024. "Persistence in financial connectedness and systemic risk," European Journal of Operational Research, Elsevier, vol. 314(1), pages 393-407.
    14. Emmanuel Dhyne & Ayumu Ken Kikkawa & Glenn Magerman, 2022. "Imperfect Competition in Firm-to-Firm Trade," Journal of the European Economic Association, European Economic Association, vol. 20(5), pages 1933-1970.
    15. Jennifer La'O & Alireza Tahbaz‐Salehi, 2022. "Optimal Monetary Policy in Production Networks," Econometrica, Econometric Society, vol. 90(3), pages 1295-1336, May.
    16. Anja Kukuvec & Harald Oberhofer, 2018. "The Propagation of Business Sentiment within the European Union?," WIFO Working Papers 549, WIFO.
    17. Niko Hauzenberger & Michael Pfarrhofer, 2021. "Bayesian State‐Space Modeling for Analyzing Heterogeneous Network Effects of US Monetary Policy," Scandinavian Journal of Economics, Wiley Blackwell, vol. 123(4), pages 1261-1291, October.
    18. Schneider, Nicolas & Strielkowski, Wadim, 2023. "Modelling the unit root properties of electricity data—A general note on time-domain applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    19. Pasten, Ernesto & Schoenle, Raphael & Weber, Michael, 2020. "The propagation of monetary policy shocks in a heterogeneous production economy," Journal of Monetary Economics, Elsevier, vol. 116(C), pages 1-22.
    20. Mandel, Antoine & Veetil, Vipin P., 2021. "Monetary dynamics in a network economy," Journal of Economic Dynamics and Control, Elsevier, vol. 125(C).

    More about this item

    Keywords

    Bayesian VAR model; monetary policy shocks; panel ARDL model; production network;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:ehl:lserod:125580. 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: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.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.