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A Data-Rich Measure of Underlying Inflation for Brazil

Author

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  • Vicente da Gama Machado
  • Raquel Nadal
  • Fernando Ryu Ramos Kawaoka

Abstract

This paper proposes a new measure of underlying inflation for Brazil based on a generalized dynamic factor model (GDFM). The approach summarizes a wide set of indicators, which the Banco Central do Brasil (BCB) regularly monitors in its assessment of the inflation scenario, such as data on prices, activity, financial and monetary variables. Differently from most core inflation approaches, the model takes account of the time series dimension – by extracting the lower frequency component – as well as the cross-section dimension and is able to handle end-of-sample unbalances. To our knowledge, it is the first application of this procedure for Brazil. The resulting series exhibits lower variability, unbiasedness and a relatively good forecasting performance compared to various other measures of trend inflation. Overall, the findings suggest the novel underlying inflation measure may be an important complement to the information set used by the BCB.

Suggested Citation

  • Vicente da Gama Machado & Raquel Nadal & Fernando Ryu Ramos Kawaoka, 2020. "A Data-Rich Measure of Underlying Inflation for Brazil," Working Papers Series 516, Central Bank of Brazil, Research Department.
    • Handle: RePEc:bcb:wpaper:516
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    File URL: https://www.bcb.gov.br/content/publicacoes/WorkingPaperSeries/wps516.pdf
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    References listed on IDEAS

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    1. Cristadoro, Riccardo & Forni, Mario & Reichlin, Lucrezia & Veronese, Giovanni, 2005. "A Core Inflation Indicator for the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 539-560, June.
    2. da Silva Filho, Tito Nícias Teixeira & Figueiredo, Francisco Marcos Rodrigues, 2011. "Has Core Inflation Been Doing a Good Job in Brazil?," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 65(2), June.
    3. Jean Boivin & Marc P. Giannoni & Ilian Mihov, 2009. "Sticky Prices and Monetary Policy: Evidence from Disaggregated US Data," American Economic Review, American Economic Association, vol. 99(1), pages 350-384, March.
    4. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
    5. Ferreira, Pedro Costa & Mattos, Daiane Marcolino de & Ardeo, Vagner Laerte, 2017. "Triple-Filter core inflation: a measure of the inflation trajectory," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 71(4), December.
    6. repec:fgv:epgrbe:v:65:n:2:a:5 is not listed on IDEAS
    7. repec:zbw:bofitp:2018_011 is not listed on IDEAS
    8. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    9. Cogley, Timothy, 2002. "A Simple Adaptive Measure of Core Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 34(1), pages 94-113, February.
    10. Tito Nícias Teixeira da Silva Filho & Francisco Marcos Rodrigues Figueiredo, 2014. "A Volatility and Persistence-Based Core Inflation," Working Papers Series 367, Central Bank of Brazil, Research Department.
    11. Tito Nícias Teixeira da Silva Filho & Francisco Marcos Rodrigues Figueiredo, 2014. "Revisitando as Medidas de Núcleo de Inflação do Banco Central do Brasil," Working Papers Series 356, Central Bank of Brazil, Research Department.
    12. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
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    Cited by:

    1. Carlomagno, Guillermo & Fornero, Jorge & Sansone, Andrés, 2023. "A proposal for constructing and evaluating core inflation measures," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(3).

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