Measuring Output Gap Uncertainty

Anthony Garratt (), James Mitchell and Shaun Vahey

No 909, Birkbeck Working Papers in Economics and Finance from Birkbeck, Department of Economics, Mathematics & Statistics

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.

Date: 2009-10
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-for and nep-mac
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Citations: View citations in EconPapers (12)

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https://eprints.bbk.ac.uk/id/eprint/7613 First version, 2009 (application/pdf)

Related works:
Working Paper: Measuring Output Gap Uncertainty (2010)
Working Paper: Measuring Output Gap Uncertainty (2009)
Working Paper: Measuring output gap uncertainty (2009)
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