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The authors would like to thank Fabio Araujo, Andre Minella, Rogelio Morales, Rodrigo Garcia-Verdu, Ahmat Jidoud, Juan Sole, Irineu de Carvalho, Ari Aisen, Michael Atingi Ego and Ali Mansoor for valuable comments and suggestions as well as other seminar participants at the Research Department of the Central Bank of Brazil and at the African and Western Hemisphere Departments at the IMF.
Obviously, one can also take different percentiles from each tail of the distribution. This is precisely what the so-called asymmetric trimmed mean core does.
For some dreadful evidence on how the exclusion of items facing persistent price changes could produce a permanent and large wedge between core and headline inflation price indices see section 4 of da Silva Filho (2008).
One can argue that oil prices should be excluded since they are “…for the most part, beyond the control of the central bank.” (Blinder, 1997). However, this fact does not prevent pressure in those prices from disseminating through the economy. Moreover, using such rationale all commodity prices should be excluded, and not only oil prices.
More rigoursly, the relative price increase is expected to be completely reversed.
IPCA stands for Broad Consumer Price Index. It is the official inflation-targeting index in Brazil. Its components are defined, respectively, from the most to the least aggregation level as group, sub-group, item and sub-item.
Given its outlier status it is excluded from panels B, C and D in order to improve inference and make the exposition clearer.
The correlation when the item “Tubers, Roots and Legumes” is excluded is -0.36.
The regression of volatility on a constant and persistence produces a slope coefficient with a t-statistic equals to -0.96.
Figure panels are lettered notionally as A, B, C and D, row by row.
It should be mentioned that while taking persistence formally into account is a new issue in the core inflation literature, there is a large literature concerned with the dynamic properties of inflation, mainly its persistence (see Fuhrer, 2010). Indeed, the efforts to better understand the persistence of inflation, its causes, as well as the effects of aggregation culminated in the creation of the Inflation Persistence Network (IPN) (see Altissimo et al., 2006).
This general expression fits most core inflation approaches, since they are essentially methods that reweigh the prices included in the headline index. To a summary of alternative ways to build core inflation measures, see Silver (2007) and Wynne (2008).
The volatility-weighted index that is obtained using
The next section explains how volatility is calculated.
Since March 2010, the Brazilian Central Bank has been releasing in its quarterly Inflation Report a double weighted core inflation measure based on volatility. The methodology of this core measure is described in da Silva Filho and Figueiredo (2011).
As pointed out by an anonymous referee, the weighting scheme given by equation (9) is a particular case of
We would like to thank Fabio Araujo, who has suggested this approach.
See footnote 6.
A detailed description of the adjustments is available upon request.
After seasonally adjusting the data only two items presented average (marginal) negative persistence (−0.03 and −0.06 for residential electricity and courses, respectively). This evidence suggests that findings of negative persistence are largely an outcome of misspecification.
Note, therefore, that there is some overlap here, since the IPCA-DP is a member of the above family (equation 7).
These two measures have been published since the March 2010 Inflation Report and their methodologies are fully described in da Silva Filho and Figueiredo (2011).
This measure has been published since the Inflation Report of March 2001. For a description of the methodology, see Figueiredo (2001).
The good performance of the smoothed trimmed mean core in this criterion should be regarded with a grain of salt, since as shown in da Silva Filho and Figueiredo (2011) this measure has produced errors with poor dynamics (i.e. it sizably underpredicted as well as overpredicted inflation during long periods).
Estimates for Brazil suggest that changes in the interest rate begin to act upon the inflation rate in a statistically significant way somewhere between six and twelve months ahead. See, for example, Bogdanski et al. (2000).
Regardless of the frequency of the data used in the models, the core inflation measures used in this paper are constructed using monthly data, which are then aggregated to lower frequency data.
Another interesting piece of evidence would be to analyze the directional forecast accuracy of the core measures (see Da Silva Filho, 2012). However, such an analysis would require its own paper.