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The FRBNY Staff Underlying Inflation Gauge: UIG

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  • Marlene Amstad
  • Simon Potter
  • Robert Rich

Abstract

Monetary policymakers and long-term investors would benefit greatly from a measure of underlying inflation that uses all relevant information, is available in real-time, and forecasts inflation better than traditional underlying inflation measures such as core inflation measures. This paper presents the "Federal Reserve Bank of New York (FRBNY) Staff Underlying Inflation Gauge (UIG)" for CPI and PCE. Using a dynamic factor model approach, the UIG is derived from a broad data set that extends beyond price series to include a wide range of nominal, real, and financial variables. It also considers the specific and time-varying persistence of individual subcomponents of an inflation series. An attractive feature of the UIG is that it can be updated on a daily basis, which allows for a close monitoring of changes in underlying inflation. This capability can be very useful when large and sudden economic fluctuations occur, as at the end of 2008. In addition, the UIG displays greater forecast accuracy than traditional measures of core inflation.

Suggested Citation

  • Marlene Amstad & Simon Potter & Robert Rich, 2014. "The FRBNY Staff Underlying Inflation Gauge: UIG," BIS Working Papers 453, Bank for International Settlements.
  • Handle: RePEc:bis:biswps:453
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    Cited by:

    1. Elena Deryugina & Alexey Ponomarenko & Andrey Sinyakov & Constantine Sorokin, 2018. "Evaluating underlying inflation measures for Russia," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 11(2), pages 124-145, May.
    2. Elena Deryugina & Alexey Ponomarenko, 2020. "Disinflation and Reliability of Underlying Inflation Measures," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(1), pages 91-111, March.
    3. Marlene Amstad & Ye Huan & Guonan Ma, 2014. "Developing an underlying inflation gauge for China," Working Papers 853, Bruegel.
    4. Bjarni G. Einarsson, 2014. "A Dynamic Factor Model for Icelandic Core Inflation," Economics wp67, Department of Economics, Central bank of Iceland.
    5. Bańbura, Marta & Bobeica, Elena, 2020. "PCCI – a data-rich measure of underlying inflation in the euro area," Statistics Paper Series 38, European Central Bank.
    6. The People's Bank of China, 2016. "An underlying inflation gauge (UIG) for China," BIS Papers chapters, in: Bank for International Settlements (ed.), Inflation mechanisms, expectations and monetary policy, volume 89, pages 117-121, Bank for International Settlements.
    7. Eliana R. González-Molano & Ramón Hernández-Ortega & Edgar Caicedo-García & Nicolás Martínez-Cortés & Jose Vicente Romero & Anderson Grajales-Olarte, 2020. "Nueva Clasificación del BANREP de la Canasta del IPC y revisión de las medidas de Inflación Básica en Colombia," Borradores de Economia 1122, Banco de la Republica de Colombia.

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    More about this item

    Keywords

    Inflation; Dynamic Factor Models; Core Inflation; Monetary Policy; Forecasting;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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