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Does monetary policy credibility mitigate the effects of uncertainty about exchange rate on uncertainties about both inflation and interest rate?

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Abstract

In the present paper, we investigate the effects of the disagreement in expectations about exchange rate on the disagreements in expectations about inflation and about monetary policy interest rate in Brazil. The analysis seeks to verify whether the uncertainty related to the future behavior of the exchange rate affects both the process of inflation expectation formation and the process of monetary policy interest rate expectation formation. Besides, the paper investigates the role of monetary policy credibility in mitigating the effects of the disagreement in expectations about exchange rate on the disagreements in expectations about inflation and about monetary policy interest rate. The results indicate that the disagreement in expectations about exchange rate impacts the disagreements in expectations about both inflation and monetary policy interest rate. In addition, the findings indicate that credibility is capable of mitigating the transmission of uncertainties about the exchange rate to uncertainties about the inflation rate and the monetary policy interest rate. Thus, our estimates reveal that (i) the marginal effect of disagreement in expectations about exchange rate on the disagreement in expectations about inflation decreases with the level of credibility and, (ii) the marginal effect of disagreement in expectations about exchange rate on the disagreement in expectations about monetary policy interest rate decreases as credibility increases.

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Notes

  1. The CBB release the maximum, minimum, median, mean, coefficient of variation and standard deviation statistics of the distribution of the daily forecast for the exchange rate, inflation rate and monetary policy interest rate in fixed event for the end of the current year and 4 years ahead.

  2. This instant is characterized by a specific date, namely, a day d, a month m and a year a.

  3. The number of agents in I is I.

  4. j = 0: current year; j = 1: next year immediately after the current year; j = 2: 2 years after the current year; j = 3: 3 years after the current year; j = 4: 2 years after the current year.

  5. Like Oliveira and Curi (2016), we use this measure of disagreement throughout the paper, as other measures require the knowledge of the entire distribution of expectations. Such information is not provided by the CBB. We are aware of the fact that papers on disagreement often use other measures, such as the inter-quartile range and Kulback-Liebler divergence measure. These two options, though, cannot be calculated without the entire distribution of individual forecasts. The standard deviation – SD(ND) – and the coefficient of variation – CV(ND) – are also frequently used as measures of disagreement. Nevertheless, although these alternative measures are released, the interpolation of the SD(ND) and CV(ND) to transform in fixed horizon is not appropriate for the analysis (see, for instance, Oliveira and Curi (2016)). Thus, it is not possible to re-estimate the equations with such measures.

  6. An example could help to clarify this issue. Suppose that an agent, in March 2005, computes his expectation about the value of the inflation rate in the end of 2005. In this case we can say that the time horizon of the forecast is 10 months because the first 2 months of 2005 have already passed and budget balance figures for January and February are known. By the same line of reasoning, when this agent computes his inflation rate expectation in September 2005 about the value of the budget balance at the closing of 2005, the time horizon of his forecast decreases to only 4 months.

  7. Indeed, the disagreement measure observed in March 2005 for the value that the inflation rate will take in the end of 2005 tends to be greater than the disagreement measure observed in September 2005 for the value that the same variable will take at the closing of 2005. The divergence measure tends to increase again in March 2006, since the current year becomes 2006 and the time horizon of the forecast becomes 9 months.

  8. When we analyze the graphs for the correlations between DISAG_EXCH and DISAG_SELIC, and DISAG_EXCH and DISAG_IPCA, we observe a small cluster of points. The existence of these upper set of points regards the period of confidence crisis due to the 2002 presidential election (“Lula effect”). Without the upper cluster, when we remove these 10 points, the regression lines still present positive inclinations, meaning that DISAG_EXCH and DISAG_SELIC, as well as DISAG_EXCH and DISAG_IPCA are positive correlated.

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Authors and Affiliations

  1. Department of Economics, and National Council for Scientific and Technological Development (CNPq) and CEMAFIN, Universidade Federal Fluminense, Rua Alexandre Moura, Bloco F, 8 - São Domingos, Niterói, RJ, 24210-200, Brazil

    Gabriel Caldas Montes

  2. Department of Economics, and CEMAFIN, Universidade Federal Fluminense, Rua Alexandre Moura, Bloco F, 8 - São Domingos, Niterói, RJ, 24210-200, Brazil

    Caio Ferrari Ferreira

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  1. Gabriel Caldas Montes
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Correspondence to Gabriel Caldas Montes.

Appendix

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Table 6 Unit root tests
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Table 7 Lag order for Granger causality tests
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Montes, G.C., Ferreira, C.F. Does monetary policy credibility mitigate the effects of uncertainty about exchange rate on uncertainties about both inflation and interest rate?. Int Econ Econ Policy 16, 649–678 (2019). https://doi.org/10.1007/s10368-018-0419-5

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