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Missing Aggregate Dynamics: On the Slow Convergence of Lumpy Adjustment Models

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Abstract

The dynamic response of aggregate variables to shocks is one of the central concerns of applied macroeconomics. The main measurement procedure for these dynamics consists of estimmiating an ARMA or VAR (VARs, for short). In non- or semi-structural approaches, the characterization of dynamics stops there. In other, more structural approaches, researcher try to uncover underlying adjustment cost parameters from the estimated VARs. Yet, in others, such as in RBC models, these estimates are used as the benchmark over which the success of the calibration exercise, and the need for further theorizing, is assessed. The main point of this paper is that when the microeconomic adjustment underlying the corresponding aggregates is lumpy, conventional VARs procedures are often inadequate for all of the above practices. In particular, the researcher will conclude that there is less persistence in the response of aggregate variables to aggregate shocks than there really is. Paradoxically, while idiosyncratic productivity and demand shocks smooth away microeconomic non-convexities and are often used as a justification for approximating aggregate dynamics with linear models, their presence exacerbate the bias. Since in practice idiosyncratic uncertainty is many times larger than aggregate uncertainty, we conclude that the problem of missing aggregate dynamics is prevalent in empirical and quantitative macroeconomic research.

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Bibliographic Info

Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1430.

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Length: 32 pages
Date of creation: Jul 2003
Date of revision: Apr 2008
Handle: RePEc:cwl:cwldpp:1430

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Keywords: Speed of adjustment; Discrete adjustment; Lumpy adjustment; Aggregation; Calvo model; ARMA process; Partial adjustment; Expected response time; Monetary policy; Investment; Labor demand; Sticky prices; Idiosyncratic shocks; Impulse response function; Time-to-build;

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References

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  1. Michael Woodford, 1999. "Optimal monetary policy inertia," Proceedings, Federal Reserve Bank of San Francisco.
  2. Saman Majd & Robert S. Pindyck, 1985. "Time to Build, Option Value, and Investment Decisions," NBER Working Papers 1654, National Bureau of Economic Research, Inc.
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  4. Ricardo J. Caballero & Eduardo Engel & John Haltiwanger, 1996. "Aggregate Employment Dynamics: Building from Microeconomic Evidence," Documentos de Trabajo 6, Centro de Economía Aplicada, Universidad de Chile.
  5. Thomas J. Sargent, 1978. "Estimation of dynamic labor demand schedules under rational expectations," Staff Report 27, Federal Reserve Bank of Minneapolis.
  6. Marvin Goodfriend, 1986. "Interest rate smoothing and price level trend-stationarity," Working Paper 86-04, Federal Reserve Bank of Richmond.
  7. Mark Bils & Peter J. Klenow, 2002. "Some Evidence on the Importance of Sticky Prices," NBER Working Papers 9069, National Bureau of Economic Research, Inc.
  8. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
  9. Brian Sack, 1998. "Uncertainty, learning, and gradual monetary policy," Finance and Economics Discussion Series 1998-34, Board of Governors of the Federal Reserve System (U.S.).
  10. Woodford, Michael, 1999. "Optimal monetary policy inertia," CFS Working Paper Series 1999/09, Center for Financial Studies (CFS).
  11. Julio Rotemberg, 1987. "The New Keynesian Microfoundations," NBER Chapters, in: NBER Macroeconomics Annual 1987, Volume 2, pages 69-116 National Bureau of Economic Research, Inc.
  12. Ricardo J. Caballero & Eduardo M. R. A. Engel & John C. Haltiwanger, 1995. "Plant-Level Adjustment and Aggregate Investment Dynamics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 26(2), pages 1-54.
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