The Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time

By Athanasios Orphanides, S. van Norden. 2004

293 citations recorded by the source corpus.

A stable predictive relationship between inflation and the output gap, often referred to as a Phillips curve, provides the basis for countercyclical monetary policy in many models. In this paper, we evaluate the usefulness of alternative univariate and multivariate estimates of the output gap for predicting inflation. Many of the ex post output gap measures we examine appear to be quite useful for predicting inflation. However, forecasts using real-time estimates of the same measures do not perform nearly as well. 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. Forecast performance also appears to be unstable over time, with models often performing differently over periods of high and low inflation. These results call into question the practical usefulness of the output gap concept for forecasting inflation.

View Paper: https://www.semanticscholar.org/paper/8a5dca79dd64405a07a2930eb01b8766f58207ec

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Citations in the corpus

• Why Has U.S. Inflation Become Harder to Forecast (2007, unknown): “as a predictor and omits a gap term, is the activity-based inflation forecast recommended by Orphanides and van Norden (2005) .”
• Short-run price forecast performance of individual and composite models for 496 corn cash markets (2017, unknown): “10 Permitting the lag structure update as forecasting moves forward has been used in economic studies such as [27,52,65] and allows for possible time variations in the number of lags.”
• Survey Measures of Expected Inflation: Revisiting the Issues of Predictive Content and Rationality (2002, background): “The recent work in Orphanides and van Norden (2002) shows that real-time estimates of the level of the output gap are generally subject to significant revisions.”
• Improving Forecast Accuracy by Combining Recursive and Rolling Forecasts (2008, unknown): “18 which we report results, is the reverse order CUSUM (of squares) method proposed by Pesaran and Timmermann (2002a), which involves searching backward from each forecast date to find the most recent break.(9) Because the reverse CUSUM proves to be prone to spurious break findings, a relatively par”