This paper investigates the statistical properties of the U.S. sacrifice ratio-the cumulative output loss arising from a permanent reduction in inflation. We derive estimates of the sacrifice ratio from three structural VAR models and then conduct Monte Carlo simulations to analyze their sampling distribution. While the point estimates of the sacrifice ratio confirm the results reported in earlier studies, we find that the estimates are very imprecise and that the degree of imprecision increases with the complexity of the model used. That is, increases in the number of structural shocks widen our confidence intervals. We conclude that the estimates provide a very unreliable guide for assessing the output cost of a disinflation policy.
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AbstractWe use matched point and density forecasts of output growth and infl ation from the ECB Survey of Professional Forecasters to derive measures of forecast uncertainty, forecast dispersion, and forecast accuracy. We construct uncertainty measures from aggregate density functions as well as from individual histograms. The uncertainty measures display countercyclical behavior, and there is evidence of increased uncertainty for output growth and infl ation since 2007. The results also indicate that uncertainty displays a very weak relationship with forecast dispersion, corroborating the fi ndings of other recent studies indicating that disagreement is not a valid proxy for uncertainty. In addition, we fi nd no correspondence between movements in uncertainty and predictive accuracy, suggesting that time-varying conditional variance estimates may not provide a reliable proxy for uncertainty. Last, using a regression equation that can be interpreted as a (G)ARCH-M-type model, we fi nd limited evidence of linkages between uncertainty and levels of infl ation and output growth.
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AbstractThis paper provides a review of the concept of core inflation and evaluates the performance of several proposed measures. We first consider the rationale of a central bank in setting its inflation goal in terms of a selected rate of consumer price growth and the use of a core inflation measure as a means of achieving this long-term policy objective. We then discuss desired attributes of a core measure of inflation, such as ease of design, accuracy in tracking trend inflation, and predictive content for future movements in aggregate inflation. Using these attributes as criteria, we evaluate several candidate series that have been proposed as core measures of consumer price index (CPI) inflation and personal consumption expenditure (PCE) inflation for the United States. The candidate series are inflation excluding food and energy, inflation excluding energy, and median inflation, as well as exponentially smoothed versions of aggregate inflation and the aforementioned individual series.For PCE inflation, we examine quarterly data starting in 1959. Unlike previous research, we confine our analysis to the methodologically consistent CPI index, which is only available starting in 1978. We find that most of the candidate series, including the familiar ex-food and energy measure, demonstrate the ability to match the mean rate of aggregate inflation and track movements in its underlying trend. In the within-sample analysis, we find that core measures derived through exponential smoothing, in combination with simple measures of economic slack, have substantial explanatory content for changes in aggregate inflation several years in advance. In the out-of-sample analysis, however, we find that no measure performs consistently well in forecasting inflation. Moreover, we document evidence of some parameter instability in the estimated forecasting models. Taken together, our findings lead us to conclude that there is no individual measure of core inflation that can be considered superior to other measures.
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