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The Demand Origins of Business Cycles

Author

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  • Christian Matthes

    (Federal Reserve Bank of Richmond)

  • Felipe Schwartzman

    (Federal Reserve Bank Richmond)

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 (Hi-VAR) model, encompassing aggregate and sectoral variables. We find that shocks whose impact originate in the “demand” side (monetary, household and government consumption) account for 2.4 times more of the variance of U.S. GDP growth at business cycle frequencies then 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 1.4 times as much as those same supply shocks.

Suggested Citation

  • Christian Matthes & Felipe Schwartzman, 2019. "The Demand Origins of Business Cycles," 2019 Meeting Papers 1122, Society for Economic Dynamics.
    • Handle: RePEc:red:sed019:1122
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    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, President and Fellows of Harvard College, 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," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 665-696.
    10. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    11. Martin Beraja & Erik Hurst & Juan Ospina, 2019. "The Aggregate Implications of Regional Business Cycles," Econometrica, Econometric Society, vol. 87(6), pages 1789-1833, November.
    12. 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.
    13. Schwartzman, Felipe, 2014. "Time to produce and emerging market crises," Journal of Monetary Economics, Elsevier, vol. 68(C), pages 37-52.
    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. Scott L. Fulford & Felipe Schwartzman, 2020. "The Benefits of Commitment to a Currency Peg: Aggregate Lessons from the Regional Effects of the 1896 U.S. Presidential Election," The Review of Economics and Statistics, MIT Press, vol. 102(3), pages 600-616, July.
    17. George-Marios Angeletos & Fabrice Collard & Harris Dellas, 2020. "Business-Cycle Anatomy," American Economic Review, American Economic Association, vol. 110(10), pages 3030-3070, October.
    18. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
    19. 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.
    20. De Graeve, Ferre & Karas, Alexei, 2010. "Identifying VARs through Heterogeneity: An Application to Bank Runs," Working Paper Series 244, Sveriges Riksbank (Central Bank of Sweden).
    21. 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.
    22. 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.
    23. 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.
    24. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    25. 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.
    26. 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.
    27. Emi Nakamura & Jón Steinsson, 2008. "Five Facts about Prices: A Reevaluation of Menu Cost Models," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 123(4), pages 1415-1464.
    28. Faust, Jon, 1998. "The robustness of identified VAR conclusions about money," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 49(1), pages 207-244, December.
    29. 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.
    30. Nadav Ben Zeev & Evi Pappa, 2017. "Chronicle of a War Foretold: The Macroeconomic Effects of Anticipated Defence Spending Shocks," Economic Journal, Royal Economic Society, vol. 127(603), pages 1568-1597, August.
    31. Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
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    Cited by:

    1. Ozdemir, Dicle, 2019. "Sectoral Business Cycle Asymmetries and Regime Shifts: Evidence from Turkey," Asian Journal of Applied Economics, Kasetsart University, Center for Applied Economics Research, vol. 26(2), December.

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