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Estimating the cross-sectional distribution of price stickiness from aggregate data

  • Carlos Carvalho
  • Niels Arne Dam

We estimate a multisector sticky-price model for the U.S. economy in which the degree of price stickiness is allowed to vary across sectors. For this purpose, we use a specification that allows us to extract information about the underlying cross-sectional distribution from aggregate data. Identification is possible because sectors play different roles in determining the response of aggregate variables to shocks at different frequencies: Sectors where prices are stickier are relatively more important in determining the low-frequency response. Estimating the model using only aggregate data on nominal and real output, we find that the inferred distribution of price stickiness is strikingly similar to the empirical distribution constructed from the recent microeconomic evidence on price setting in the U.S. economy. We also provide macro-based estimates of the underlying distribution for ten other countries. Finally, we explore our Bayesian approach to combine the aggregate time-series data with the microeconomic information on the distribution of price rigidity. Our results show that allowing for this type of heterogeneity is critically important to understanding the joint dynamics of output and prices, and it constitutes a step toward reconciling the extent of nominal price rigidity implied by aggregate data with the evidence from microeconomic data on price stickiness.

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Paper provided by Federal Reserve Bank of New York in its series Staff Reports with number 419.

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Date of creation: 2009
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Handle: RePEc:fip:fednsr:419
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  1. Sbordone, Argia M., 2002. "Prices and unit labor costs: a new test of price stickiness," Journal of Monetary Economics, Elsevier, vol. 49(2), pages 265-292, March.
  2. Taylor, John B, 1979. "Staggered Wage Setting in a Macro Model," American Economic Review, American Economic Association, vol. 69(2), pages 108-13, May.
  3. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, 09.
  4. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
  5. Huw Dixon & Engin Kara, 2007. "Persistence and Nominal Inertia in a Generalized Taylor Economy: How Longer Contracts Dominate Shorter Contracts," Discussion Papers 07-01, Department of Economics, University of Birmingham.
  6. Aoki, Kosuke, 2001. "Optimal monetary policy responses to relative-price changes," Journal of Monetary Economics, Elsevier, vol. 48(1), pages 55-80, August.
  7. Esteban Jadresic, 1999. "Sticky Prices; An Empirical Assessment of Alternative Models," IMF Working Papers 99/72, International Monetary Fund.
  8. N. Gregory Mankiw & Ricardo Reis, 2001. "Sticky information versus sticky prices: a proposal to replace the New-Keynesian Phillips curve," Proceedings, Federal Reserve Bank of San Francisco, issue Jun.
  9. Emmanuel Dhyne & Luis J. Alvarez & Herve Le Bihan & Giovanni Veronese & Daniel Dias & Johannes Hoffmann & Nicole Jonker & Patrick Lunnemann & Fabio Rumler & Jouko Vilmunen, 2006. "Price Changes in the Euro Area and the United States: Some Facts from Individual Consumer Price Data," Journal of Economic Perspectives, American Economic Association, vol. 20(2), pages 171-192, Spring.
  10. Mark Bils & Peter J. Klenow, 2002. "Some Evidence on the Importance of Sticky Prices," NBER Working Papers 9069, National Bureau of Economic Research, Inc.
  11. Benigno, Pierpaolo, 2004. "Optimal monetary policy in a currency area," Journal of International Economics, Elsevier, vol. 63(2), pages 293-320, July.
  12. Felipe Schwartzman & Carlos Carvalho, 2008. "Heterogeneous Price Setting Behavior and Monetary Non-neutrality: Some General Results," 2008 Meeting Papers 1040, Society for Economic Dynamics.
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