IDEAS home Printed from https://ideas.repec.org/p/fip/fedrwp/19-09.html
   My bibliography  Save this paper

What Do Sectoral Dynamics Tell Us About the Origins of Business Cycles?

Author

Listed:
  • Christian Matthes
  • Felipe Schwartzman

Abstract

We use economic theory to rank the impact of structural shocks across sectors. This ranking helps us to identify the origins of U.S. business cycles. To do this, we introduce a Hierarchical Vector Auto-Regressive model, encompassing aggregate and sectoral variables. We find that shocks whose impact originate in the \"demand\" side (monetary, household, and government consumption) account for 43 percent more of the variance of U.S. GDP growth at business cycle frequencies than identified shocks originating in the \"supply\" side (technology and energy). Furthermore, corporate financial shocks, which theory suggests propagate to large extent through demand channels, account for an amount of the variance equal to an additional 82 percent of the fraction explained by these supply shocks.

Suggested Citation

  • Christian Matthes & Felipe Schwartzman, 2019. "What Do Sectoral Dynamics Tell Us About the Origins of Business Cycles?," Working Paper 19-9, Federal Reserve Bank of Richmond.
    • Handle: RePEc:fip:fedrwp:19-09
    as

    Download full text from publisher

    File URL: https://www.richmondfed.org/-/media/richmondfedorg/publications/research/working_papers/2019/wp19-09.pdf
    File Function: Full text
    Download Restriction: no

    References listed on IDEAS

    as
    1. Alexander Chudik & M. Hashem Pesaran, 2016. "Theory And Practice Of Gvar Modelling," Journal of Economic Surveys, Wiley Blackwell, vol. 30(1), pages 165-197, February.
    2. 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.
    3. Sean Holly & Ivan Petrella, 2012. "Factor Demand Linkages, Technology Shocks, and the Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 948-963, November.
    4. Karel Mertens & Morten O. Ravn, 2013. "The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States," American Economic Review, American Economic Association, vol. 103(4), pages 1212-1247, June.
    5. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    6. Valerie A. Ramey, 2011. "Identifying Government Spending Shocks: It's all in the Timing," The Quarterly Journal of Economics, Oxford University Press, vol. 126(1), pages 1-50.
    7. Justiniano, Alejandro & Primiceri, Giorgio E. & Tambalotti, Andrea, 2010. "Investment shocks and business cycles," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 132-145, March.
    8. Jean Boivin & Marc P. Giannoni & Ilian Mihov, 2009. "Sticky Prices and Monetary Policy: Evidence from Disaggregated US Data," American Economic Review, American Economic Association, vol. 99(1), pages 350-384, March.
    9. Juan F. Rubio-Ramírez & Daniel F. Waggoner & Tao Zha, 2010. "Structural Vector Autoregressions: Theory of Identification and Algorithms for Inference," Review of Economic Studies, Oxford University Press, vol. 77(2), pages 665-696.
    10. Martin Beraja & Erik Hurst & Juan Ospina, 2019. "The Aggregate Implications of Regional Business Cycles," Econometrica, Econometric Society, vol. 87(6), pages 1789-1833, November.
    11. Schwartzman, Felipe, 2014. "Time to produce and emerging market crises," Journal of Monetary Economics, Elsevier, vol. 68(C), pages 37-52.
    12. Ernesto Pasten & Raphael S. Schoenle & Michael Weber & Michael Weber, 2018. "The Propagation of Monetary Policy Shocks in a Heterogeneous Production Economy," CESifo Working Paper Series 7376, CESifo.
    13. Altonji, Joseph G & Ham, John C, 1990. "Variation in Employment Growth in Canada: The Role of External, National, Regional, and Industrial Factors," Journal of Labor Economics, University of Chicago Press, vol. 8(1), pages 198-236, January.
    14. Pooyan Amir-Ahmadi & Thorsten Drautzburg, 2017. "Identification Through Heterogeneity," Working Papers 17-11, Federal Reserve Bank of Philadelphia.
    15. Rajan, Raghuram G & Zingales, Luigi, 1998. "Financial Dependence and Growth," American Economic Review, American Economic Association, vol. 88(3), pages 559-586, June.
    16. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    17. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    18. Bachmann, Rüdiger & Zorn, Peter, 2020. "What drives aggregate investment? Evidence from German survey data," Journal of Economic Dynamics and Control, Elsevier, vol. 115(C).
    19. Kocięcki, Andrzej, 2010. "A Prior for Impulse Responses in Bayesian Structural VAR Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 115-127.
    20. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    21. Canova, Fabio & Nicolo, Gianni De, 2002. "Monetary disturbances matter for business fluctuations in the G-7," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1131-1159, September.
    22. Dario Caldara & Edward Herbst, 2019. "Monetary Policy, Real Activity, and Credit Spreads: Evidence from Bayesian Proxy SVARs," American Economic Journal: Macroeconomics, American Economic Association, vol. 11(1), pages 157-192, January.
    23. James H. Stock & Mark W. Watson, 2005. "Understanding Changes In International Business Cycle Dynamics," Journal of the European Economic Association, MIT Press, vol. 3(5), pages 968-1006, September.
    24. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    25. Emi Nakamura & Jón Steinsson, 2008. "Five Facts about Prices: A Reevaluation of Menu Cost Models," The Quarterly Journal of Economics, Oxford University Press, vol. 123(4), pages 1415-1464.
    26. Christiane Baumeister & James D. Hamilton, 2015. "Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information," Econometrica, Econometric Society, vol. 83(5), pages 1963-1999, September.
    27. Todd E. Clark & Kwanho Shin, 1998. "The sources of fluctuations within and across countries," Research Working Paper 98-04, Federal Reserve Bank of Kansas City.
    28. Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dimitris Korobilis, 2020. "Sign restrictions in high-dimensional vector autoregressions," Working Papers 2020_21, Business School - Economics, University of Glasgow.

    More about this item

    Keywords

    Aggregate Shocks; Sectoral Data; Bayesian Analysis; Impulse Responses;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

    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:fip:fedrwp:19-09. 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: (). General contact details of provider: http://edirc.repec.org/data/frbrius.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.