Working Paper: CEPR ID: DP7742
Authors: Anthony Garratt; James Mitchell; Shaun Vahey
Abstract: We propose a methodology for producing density forecasts for the output gap in real time using a large number of vector autoregessions in inflation and output gap measures. Density combination utilizes a linear mixture of experts framework to produce potentially non-Gaussian ensemble densities for the unobserved output gap. In our application, we show that data revisions alter substantially our probabilistic assessments of the output gap using a variety of output gap measures derived from univariate detrending filters. The resulting ensemble produces well-calibrated forecast densities for US inflation in real time, in contrast to those from simple univariate autoregressions which ignore the contribution of the output gap. Combining evidence from both linear trends and more flexible univariate detrending filters induces strong multi-modality in the predictive densities for the unobserved output gap. The peaks associated with these two detrending methodologies indicate output gaps of opposite sign for some observations, reflecting the pervasive nature of model uncertainty in our US data.
Keywords: density combination; ensemble forecasting; output gap uncertainty; VAR models
JEL Codes: C32; C53; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| output gap (E23) | inflation forecasts (E31) |
| detrending methods (C22) | output gap (E23) |
| data revisions (Y10) | output gap assessments (E66) |
| ensemble methodology (C90) | predictive densities for output gap (E37) |
| ensemble methodology (C90) | forecast densities for inflation (E31) |
| detrending methodologies (C22) | multimodality in predictive densities (C59) |
| output gap assessments (E66) | policymakers' conclusions (D78) |