The Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time
Orphanides, Athanasios, Van Norden, Simon, Journal of Money, Credit & Banking
A STABLE PREDICTIVE relationship between inflation and a measure of deviations of aggregate demand from the economy's potential supply--the "output gap"--provides the basis for many formulations of activist countercyclical stabilization policy. Such a relationship, referred to as a Phillips curve, is often seen as a helpful guide for policymakers aiming to maintain low inflation and stable economic growth. According to this paradigm, when aggregate demand exceeds potential output, the economy is subject to inflationary pressures and inflation should be expected to rise. Under these circumstances, policymakers aiming to contain the acceleration in prices might wish to adopt policies restricting aggregate demand. Similarly, when aggregate demand falls short of potential supply, inflation should be expected to fall, prompting policymakers to consider the adoption of expansionary policies. (1) Even assuming that the theoretical motivation for a relationship between the output gap and inflation is fundamentally correct, a number of issues may complicate its use for forecasting in practice. First, the definition of "potential output"--and the accompanying "output gap"--that might be useful in practice is far from clear. Given a definition of the output gap, its exact empirical relationship with inflation is not known a priori and would need to be determined from the data. Second, even if the proper conceptual and empirical relationships were identified, the operational usefulness of the output gap will be limited by the availability of timely and reliable estimates of the identified concept. As is well known, empirical estimates of the output gap are generally subject to significant and highly persistent revisions (for example, see Orphanides and van Norden 2002). The subsequent evolution of the economy leads to improved historical estimates of the gap by providing useful information about the state of the business cycle. As a result, considerable uncertainty regarding the value of the gap remains even long after it would be needed for forecasting inflation. This suggests that although the output gap may be quite useful for historical analysis, its practical usefulness for forecasting inflation in real time may be quite limited.
In this paper we assess the usefulness of alternative estimation methods of the output gap for predicting inflation, paying particular attention to the distinction between suggested usefulness--based on ex post analysis using revised output gaps, and operational usefulness--based on simulated real-time out-of-sample analysis. (2) First, using out-of-sample analysis based on ex post estimates of the output gap, we confirm that many concepts appear to be useful for predicting inflation. This is as would be expected since the implicit Phillips curve relationships recovered in this manner are similar to the relationships commonly found in empirical macroeconometric models. To assess their operational usefulness, we generate out-of-sample forecasts based on real-time output gap measures; those constructed using only data (and parameter estimates) available at the time forecasts are generated. (3) We compare the resulting forecasts to both autoregressive forecasts of inflation and bivariate forecasts that employ information from output growth as well as past inflation.
Our findings show that forecasts using ex post estimates of the output gap severely overstate the gap's usefulness for predicting inflation. Real-time forecasts using the output gap are often less accurate than forecasts that abstract from the output gap concept altogether. And the relative usefulness of real-time output gap estimates diminishes further when compared to simple bivariate forecasting models which use past inflation and output growth. In some cases, we find certain measures of the output gap produce superior forecasts of inflation. However, relative performance seems to vary considerably over time, with models which perform relatively well in some periods performing relatively poorly in others. …