Disinflation and Reliability of Underlying Inflation Measures

Abstract

We estimated a non-Stationary dynamic factor model and used it to generate
artificial episodes of disinflation (permanent changes in the mean inflation
rate). These datasets were used to test the forecasting abilities of alternative
underlying inflation indicators (i.e. measures that capture sustained movements
in inflation extracted from information in a disaggregated set of price data).
We found that the out of sample forecast errors of the benchmark underlying
inflation measures (based on unobserved trend extraction) are more severely
affected by disinflation than the alternative simpler methods (based on exclusion
or re-weighting approaches). We also show that a non-stationary dynamic factor
model may be employed for the extraction of the unobserved trend to be used
as an underlying inflation measure.