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Relative Goods' Prices, Pure Inflation, and the Phillips Correlation

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  • Ricardo Reis
  • Mark W. Watson

Abstract

This paper uses a dynamic factor model for the quarterly changes in consumption goods' prices in the United States since 1959 to separate them into three independent components: idiosyncratic relative-price changes, a low-dimensional index of aggregate relative-price changes, and an index of equiproportional changes in all inflation rates that we label "pure" inflation. We use the estimates to answer two questions. First, what share of the variability of inflation is associated with each component, and how are they related to conventional measures of monetary policy and relative-price shocks? Second, what drives the Phillips correlation between inflation and measures of real activity? (JEL E21, E23, E31, E52)

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Bibliographic Info

Article provided by American Economic Association in its journal American Economic Journal: Macroeconomics.

Volume (Year): 2 (2010)
Issue (Month): 3 (July)
Pages: 128-57

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Handle: RePEc:aea:aejmac:v:2:y:2010:i:3:p:128-57

Note: DOI: 10.1257/mac.2.3.128
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  1. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
  2. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
  3. 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.
  4. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
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  6. Riccardo Cristadoro & Mario Forni & Lucrezia Reichlin & Giovanni Veronese, 2001. "A core inflation index for the euro area," Temi di discussione (Economic working papers) 435, Bank of Italy, Economic Research and International Relations Area.
  7. Mishkin, Frederic S., 1992. "Is the Fisher effect for real? : A reexamination of the relationship between inflation and interest rates," Journal of Monetary Economics, Elsevier, vol. 30(2), pages 195-215, November.
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  10. Marlene Amstad & Simon Potter, 2009. "Real time underlying inflation gauges for monetary policymakers," Staff Reports 420, Federal Reserve Bank of New York.
  11. Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
  12. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
  13. Watson, Mark W., 1989. "Recursive solution methods for dynamic linear rational expectations models," Journal of Econometrics, Elsevier, vol. 41(1), pages 65-89, May.
  14. Amengual, Dante & Watson, Mark W., 2007. "Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 91-96, January.
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  16. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
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Citations

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Cited by:
  1. Gregor W. Smith, 2009. "The Missing Links: Better Measures of Inflation and Inflation Expectations in Canada," C.D. Howe Institute Commentary, C.D. Howe Institute, issue 287, April.
  2. Koopman, Siem Jan & van der Wel, Michel, 2013. "Forecasting the US term structure of interest rates using a macroeconomic smooth dynamic factor model," International Journal of Forecasting, Elsevier, vol. 29(4), pages 676-694.
  3. Kitov, Ivan & Kitov, Oleg, 2012. "Sustainable trends and periodicity in consumer price indices indicate that the era of low energy prices is approaching," MPRA Paper 43392, University Library of Munich, Germany.
  4. Mackowiak, Bartosz Adam & Moench, Emanuel & Wiederholt, Mirko, 2009. "Sectoral Price Data and Models of Price Setting," CEPR Discussion Papers 7339, C.E.P.R. Discussion Papers.
  5. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2008. "A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models," Working Papers ECARES 2008_034, ULB -- Universite Libre de Bruxelles.
  6. Tomás Castagnino & Laura D’Amato, 2008. "Regime and Underlying Inflation Dynamics: ¿Generalized Comovement or Relative Price Adjustment?," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(52), pages 87-120, October -.
  7. Mikael Khan & Louis Morel & Patrick Sabourin, 2013. "The Common Component of CPI: An Alternative Measure of Underlying Inflation for Canada," Working Papers 13-35, Bank of Canada.
  8. Michal Brzoza-Brzezina & Jacek Kotlowski, 2009. "Estimating pure inflation in the Polish economy," Working Papers 37, Department of Applied Econometrics, Warsaw School of Economics.
  9. Bai, Jushan & Ng, Serena, 2013. "Principal components estimation and identification of static factors," Journal of Econometrics, Elsevier, vol. 176(1), pages 18-29.
  10. Choi, Chi-Young & O'Sullivan, Róisín, 2013. "Heterogeneous response of disaggregate inflation to monetary policy regime change: The role of price stickiness," Journal of Economic Dynamics and Control, Elsevier, vol. 37(9), pages 1814-1832.
  11. Alan K. Detmeister, 2011. "The usefulness of core PCE inflation measures," Finance and Economics Discussion Series 2011-56, Board of Governors of the Federal Reserve System (U.S.).

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