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The common component of the CPI - A trendy measure of Icelandic underlying inflation

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  • Aðalheiður Ó. Guðlaugsdóttir
  • Lilja S. Kro

Abstract

This paper presents a new measure of underlying inflation in Iceland: the common component of the CPI. It is obtained from a factor model that is estimated using monthly data on individual subcomponents of the CPI. The results indicate that the common component is most responsive to imported inflation. Over the entire estimation period, the common component explained, on average, half of the total variation of individual subindices. Following the easing and stabilisation of inflation and inflation expectations in recent years, the majority of the variance of individual subindices is explained by sector-specific shocks over a shorter estimation period. The performance of this measure was also checked along several dimensions. It appears robust to the estimation period used, and both the level of aggregation and the impact of revisions due to real-time extraction of the common component were negligible. The common component could serve as a useful complement to existing measures of underlying inflation already monitored by the Central Bank of Iceland.

Suggested Citation

  • Aðalheiður Ó. Guðlaugsdóttir & Lilja S. Kro, 2018. "The common component of the CPI - A trendy measure of Icelandic underlying inflation," Economics wp78, Department of Economics, Central bank of Iceland.
    • Handle: RePEc:ice:wpaper:wp78
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    References listed on IDEAS

    as
    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
    2. Necati Tekatli, 2010. "A New Core Inflation Indicator for Turkey," Central Bank Review, Research and Monetary Policy Department, Central Bank of the Republic of Turkey, vol. 10(2), pages 9-21.
    3. Mikael Khan & Louis Morel & Patrick Sabourin, 2015. "A Comprehensive Evaluation of Measures of Core Inflation for Canada," Discussion Papers 15-12, Bank of Canada.
    4. Ricardo Reis & Mark W. Watson, 2010. "Relative Goods' Prices, Pure Inflation, and the Phillips Correlation," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(3), pages 128-157, July.
    5. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
    6. Thórarinn G. Pétursson, 2018. "Disinflation and improved anchoring of long-term inflation expectations - The Icelandic experience," Economics wp77, Department of Economics, Central bank of Iceland.
    7. Michael Kirker, 2010. "What drives core inflation? A dynamic factor model analysis of tradable and nontradable prices," Reserve Bank of New Zealand Discussion Paper Series DP2010/13, Reserve Bank of New Zealand.
    8. Gael Price, 2013. "Some revisions to the sectoral factor model of core inflation," Reserve Bank of New Zealand Analytical Notes series AN2013/06, Reserve Bank of New Zealand.
    9. Mikael Khan & Louis Morel & Patrick Sabourin, 2013. "The Common Component of CPI: An Alternative Measure of Underlying Inflation for Canada," Staff Working Papers 13-35, Bank of Canada.
    10. Bjarni G. Einarsson, 2014. "A Dynamic Factor Model for Icelandic Core Inflation," Economics wp67, Department of Economics, Central bank of Iceland.
    11. Antonello D’ Agostino & Domenico Giannone, 2012. "Comparing Alternative Predictors Based on Large‐Panel Factor Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(2), pages 306-326, April.
    12. 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.
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    More about this item

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

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