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Breakeven Inflation Rate Estimation: an alternative approach considering indexation lag and seasonality

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  • Flávio de Freitas Val
  • Gustavo Silva Araujo

Abstract

The breakeven inflation rate (BEIR) is the difference between nominal and real interest rates. This measure can be obtained from fixed-rate and inflation-indexed government bonds. However, this task has two difficulties: a) inflation-indexed securities have an indexation lag in respect to inflation; and b) the seasonality of inflation implies seasonality of the real rate. The objective of this paper is to propose an alternative method for BEIR estimation using only government bonds, as well as to determine if this measure contains information on future short-term inflation. A good measure of BEIR estimated directly from bonds is relevant since these are the inflation-linked instruments with the highest liquidity. The empirical results show that the proposed BEIR is superior to the BEIR that do not take into account the problems of lag-indexation and seasonality and is statistically equal to or better than survey-based inflation expectations and the BEIR extracted from the future market when forecasting short-term inflation. Furthermore, the results show the lag and seasonal adjustments are more important for the estimation of the BEIR in the short term. An advantage of the proposed BEIR in relation to survey-based inflation expectations is that it allows for real-time monitoring, since it is continually updated.

Suggested Citation

  • Flávio de Freitas Val & Gustavo Silva Araujo, 2022. "Breakeven Inflation Rate Estimation: an alternative approach considering indexation lag and seasonality," Working Papers Series 493, Central Bank of Brazil, Research Department.
  • Handle: RePEc:bcb:wpaper:493
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    File URL: https://www.bcb.gov.br/content/publicacoes/WorkingPaperSeries/WP493v3.pdf
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    References listed on IDEAS

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    Cited by:

    1. Ricardo Reis, 2021. "Losing the Inflation Anchors," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 52(2 (Fall)), pages 307-379.
    2. Araujo, Gustavo Silva & Gaglianone, Wagner Piazza, 2023. "Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(2).

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