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Asymmetric Phase Shifts in the U.S. Industrial Production Cycles

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Abstract

We identify the cyclical turning points of 74 U.S. manufacturing industries and uncover new empirical regularities: (i) Cyclical phase shifts are highly concentrated around the aggregate turning points; (ii) In contrast to the conventional notion of a sudden stop and slow recovery, troughs are much more concentrated than peaks; (iii) Occurrences of phase shifts across industries support the spillovers through input-output linkages; (iv) The common macroeconomic shocks, such as exogenous changes in the federal funds rate, government spending, and oil prices, are significant drivers of industrial phase shifts; (v) Both monetary and fiscal policy shocks are more effective in recessions.

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  • Yongsung Chang & Sunoong Hwang, 2011. "Asymmetric Phase Shifts in the U.S. Industrial Production Cycles," RCER Working Papers 564, University of Rochester - Center for Economic Research (RCER).
  • Handle: RePEc:roc:rocher:564
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    1. repec:nbr:nberch:13342 is not listed on IDEAS
    2. 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.
    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. Harding, Don & Pagan, Adrian, 2011. "An Econometric Analysis of Some Models for Constructed Binary Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 86-95.
    5. Christian Bayer & Ruediger Bachmann, 2009. "The Cross-section of Firms over the Business Cycle: New Facts and a DSGE Exploration," 2009 Meeting Papers 866, Society for Economic Dynamics.
    6. Hornstein, Andreas & Praschnik, Jack, 1997. "Intermediate inputs and sectoral comovement in the business cycle," Journal of Monetary Economics, Elsevier, vol. 40(3), pages 573-595, December.
    7. Bachmann, Rüdiger & Sims, Eric R., 2012. "Confidence and the transmission of government spending shocks," Journal of Monetary Economics, Elsevier, vol. 59(3), pages 235-249.
    8. Veldkamp, Laura & Wolfers, Justin, 2007. "Aggregate shocks or aggregate information? Costly information and business cycle comovement," Journal of Monetary Economics, Elsevier, vol. 54(Supplemen), pages 37-55, September.
    9. Lee, Lung-Fei, 1997. "Simulated maximum likelihood estimation of dynamic discrete choice statistical models some Monte Carlo results," Journal of Econometrics, Elsevier, vol. 82(1), pages 1-35.
    10. Hamilton, James D., 2003. "What is an oil shock?," Journal of Econometrics, Elsevier, vol. 113(2), pages 363-398, April.
    11. Harding, Don & Pagan, Adrian, 2002. "Dissecting the cycle: a methodological investigation," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 365-381, March.
    12. Weise, Charles L, 1999. "The Asymmetric Effects of Monetary Policy: A Nonlinear Vector Autoregression Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 31(1), pages 85-108, February.
    13. Alan J. Auerbach & Yuriy Gorodnichenko, 2012. "Measuring the Output Responses to Fiscal Policy," American Economic Journal: Economic Policy, American Economic Association, vol. 4(2), pages 1-27, May.
    14. Andreas Hornstein, 2000. "The business cycle and industry comovement," Economic Quarterly, Federal Reserve Bank of Richmond, issue Win, pages 27-48.
    15. Lutz Kilian, 2008. "Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?," The Review of Economics and Statistics, MIT Press, vol. 90(2), pages 216-240, May.
    16. Timothy G. Conley & Bill Dupor, 2003. "A Spatial Analysis of Sectoral Complementarity," Journal of Political Economy, University of Chicago Press, vol. 111(2), pages 311-352, April.
    17. Eisfeldt, Andrea L. & Rampini, Adriano A., 2006. "Capital reallocation and liquidity," Journal of Monetary Economics, Elsevier, vol. 53(3), pages 369-399, April.
    18. Kevin M. Murphy & Andrei Shleifer & Robert W. Vishny, 1989. "Building Blocks of Market Clearing Business Cycle Models," NBER Chapters,in: NBER Macroeconomics Annual 1989, Volume 4, pages 247-302 National Bureau of Economic Research, Inc.
    19. Lo, Ming Chien & Piger, Jeremy, 2005. "Is the Response of Output to Monetary Policy Asymmetric? Evidence from a Regime-Switching Coefficients Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(5), pages 865-886, October.
    20. Goolsbee, Austan & Klenow, Peter J, 2002. "Evidence on Learning and Network Externalities in the Diffusion of Home Computers," Journal of Law and Economics, University of Chicago Press, vol. 45(2), pages 317-343, October.
    21. Gert Peersman & Frank Smets, 2005. "The Industry Effects of Monetary Policy in the Euro Area," Economic Journal, Royal Economic Society, vol. 115(503), pages 319-342, April.
    22. Hess, Gregory D & Iwata, Shigeru, 1997. "Measuring and Comparing Business-Cycle Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(4), pages 432-444, October.
    23. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, January.
    24. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1, January.
    25. Cogley, Timothy & Nason, James M., 1995. "Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research," Journal of Economic Dynamics and Control, Elsevier, vol. 19(1-2), pages 253-278.
    26. Higson, C. & Holly, S. & Kattuman, P., 2002. "The cross-sectional dynamics of the US business cycle: 1950-1999," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1539-1555, August.
    27. Bester, C. Alan & Hansen, Christian, 2009. "A Penalty Function Approach to Bias Reduction in Nonlinear Panel Models with Fixed Effects," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(2), pages 131-148.
    28. Gerhard Bry & Charlotte Boschan, 1971. "Foreword to "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs"," NBER Chapters,in: Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, pages -1 National Bureau of Economic Research, Inc.
    29. Matthias Kehrig, 2011. "The Cyclicality of Productivity Dispersion," 2011 Meeting Papers 484, Society for Economic Dynamics.
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    Cited by:

    1. Sumru Altug & Fabio Canova, 2014. "Do Institutions and Culture Matter for Business Cycles?," Open Economies Review, Springer, pages 93-122.
    2. Steven Cassou & Jesús Vázquez, 2014. "Employment comovements at the sectoral level over the business cycle," Empirical Economics, Springer, vol. 46(4), pages 1301-1323, June.

    More about this item

    Keywords

    Business cycles; Comovement; Turning points; Asymmetries;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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