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A Factorial Decomposition Of Inflation In Peru, An Alternative Measure Of Core Inflation

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  • Alberto Humala

    ()

  • Gabriel Rodríguez

    ()
    (Departamento de Economía - Pontificia Universidad Católica del Perú)

Abstract

A dynamic factorial decomposition model of inflation is estimated using Peruvian monthly data for 1995:01-2008:07. This model allows identification of changes in three relevant ináation components: idiosyncratic relative prices, aggregate relative prices, and absolute prices. Furthermore, following Reis and Watson (2007), the model allows measuring pure inflation as the common factor in the inflation rate that has a proportionate effect to all prices and that is not correlated with relative price changes at any period of time. This pure inflation estimate relates closely to standard measures of core inflation. Results are robust to different lag structures and various stochastic assumptions on the estimated factors.

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Bibliographic Info

Paper provided by Departamento de Economía - Pontificia Universidad Católica del Perú in its series Documentos de Trabajo with number 2011-315.

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Length: pages
Date of creation: 2011
Date of revision:
Publication status: published
Handle: RePEc:pcp:pucwps:wp00315

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Postal: Av. Universitaria 1801, San Miguel, Lima, Perú
Phone: (511) 626-2000 ext. 4950, 4951
Fax: (511) 626-2874
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Web page: http://www.pucp.edu.pe/departamento/economia/
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Keywords: Factoral Decomposition / Pure Inflation / Core Inflation / Price Changes;

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References

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  1. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
  2. Cristadoro, Riccardo & Forni, Mario & Reichlin, Lucrezia & Veronese, Giovanni, 2001. "A Core Inflation Index for the Euro Area," CEPR Discussion Papers 3097, C.E.P.R. Discussion Papers.
  3. Boivin, Jean & Giannoni, Marc P. & Mihov, Ilian, 2006. "Sticky prices and monetary policy: Evidence from disaggregated US data," CFS Working Paper Series 2007/14, Center for Financial Studies (CFS).
  4. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
  5. Marlene Amstad & Simon Potter, 2009. "Real time underlying inflation gauges for monetary policymakers," Staff Reports 420, Federal Reserve Bank of New York.
  6. Altissimo, Filippo & Mojon, Benoit & Zaffaroni, Paolo, 2009. "Can aggregation explain the persistence of inflation?," Journal of Monetary Economics, Elsevier, vol. 56(2), pages 231-241, March.
  7. Michal Brzoza-Brzezina & Jacek Kotlowski, 2009. "Estimating pure inflation in the Polish economy," Working Papers 37, Department of Applied Econometrics, Warsaw School of Economics.
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Cited by:
  1. Wojciech Charemza & Imran Husssain Shah, 2013. "Stability price index, core inflation and output volatility," Applied Economics Letters, Taylor & Francis Journals, vol. 20(8), pages 737-741, May.

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