Measuring Inflation Forecast Uncertainty

The federal funds target range has been zero to one-quarter percent since late 2008. But a number of signs suggest that change may be drawing near. In their projections made for the March 2015 meeting, all but two Federal Open Market Committee (FOMC) participants believed that it would be appropriate to increase the federal funds rate target range in 2015. In their post-meeting statement, the Committee anticipated that "it will be appropriate to raise the target range for the federal funds rate when it has seen further improvement in the labor market and is reasonably confident that inflation will move back to its 2 percent objective over the medium term." (1)

With ongoing solid readings coming from the labor market, this Commentary examines the outlook for inflation. Looking across a range of statistical models, we consider where inflation is likely to go in the future. In doing so, we look at not only the most likely future path for inflation but also the uncertainty around that path--that is, how confident the models are about their own predictions.

Our models generally put inflation on a rising path: inflation in two years is expected to be greater than inflation in one year. In an absolute sense, the uncertainty surrounding these projections is large, but this comes as no surprise--it is well known that forecasting inflation far into the future is always difficult. (2) When we measure how uncertainty today compares with its past values, we find that inflation forecast uncertainty today is perhaps somewhat elevated compared with the norms of the last 20 years, while forecast uncertainty for core inflation is relatively low compared with the same period. But if we focus on the recent past, inflation forecast uncertainty as of the first quarter of 2015 is much lower than it was around the Great Recession, having fallen by two-thirds in some cases.

A Range of Models

Because forecasting inflation is a difficult task, no single model has emerged as "the best" forecasting tool. The simplest inflation forecasting model predicts that future inflation will be equal to its value over the past year; at the other extreme, factor models can combine hundreds of indicators to predict future inflation rates. (3) We do not attempt to examine every possible inflation forecasting model. Instead, we present results from five fairly sophisticated statistical forecasting models with good inflation forecasting properties. All of the models use data at a quarterly frequency.

First, we include a relatively standard statistical model used for macroeconomic forecasting and policy analysis: a Bayesian vector autoregression (BVAR). The model follows Knotek and Zaman (2013) and includes seven variables: real GDP, real personal consumption expenditures (PCE), the unemployment rate, unit labor cost growth, PCE inflation, core PCE inflation, and the federal funds rate. (4) We estimate this BVAR model using long time series data, starting in 1959:Q1.

Second, we examine the forecasts coming from a medium-scale vector autoregression estimated using Bayesian methods and a steady-state prior (SS-BVAR), in the spirit of Villani (2009). A key benefit of the SS-BVAR is that it allows us to incorporate additional information, potentially coming from outside the model, to inform the model's longer-run properties; but incorporating this additional information can help with shorter-term forecasting as well. To the list of variables above, we add one more labor market variable (nonfarm payroll growth) and two financial variables (the S&P 500 and the risk spread between Baa corporate bond yields and 10-year Treasury yields). (5) We estimate this SS-BVAR model using time series data starting in 1985:Q1, to account for changes in inflation dynamics that occurred around that time. (6)

Models three through five feature stochastic volatility. In most macroeconomic models, including the two listed above, the size of realized shocks can vary over time, but the shocks are assumed to be drawn from a distribution with a standard deviation that is fixed. Models with stochastic volatility allow the standard deviation of the size of the shocks to change over time. …

To read the full text of this article and others like it, subscribe today!