Real-time Inflation Forecast Densities from Ensemble Phillips Curves

Anthony Garratt (), James Mitchell, Shaun Vahey and Elizabeth Wakerly

No 910, Birkbeck Working Papers in Economics and Finance from Birkbeck, Department of Economics, Mathematics & Statistics

Abstract: A popular macroeconomic forecasting strategy takes combinations across many models to hedge against model instabilities of unknown timing; see (among others) Stock andWatson (2004) and Clark and McCracken (2009). In this paper, we examine the effectiveness of recursive-weight and equal-weight combination strategies for density forecasting using a time-varying Phillips curve relationship between inflation and the output gap. The densities reflect the uncertainty across a large number of models using many statistical measures of the output gap, allowing for a single structural break of unknown timing. We use real-time data for the US, Australia, New Zealand and Norway. Our main finding is that the recursive-weight strategy performs well across the real-time data sets, consistently giving well-calibrated forecast densities. The equal-weight strategy generates poorly-calibrated forecast densities for the US and Australian samples. There is little difference between the two strategies for our New Zealand and Norwegian data. We also find that the ensemble modeling approach performs more consistently with real-time data than with revised data in all four countries.

Date: 2009-10
New Economics Papers: this item is included in nep-cba, nep-for and nep-mac
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Citations: View citations in EconPapers (4)

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https://eprints.bbk.ac.uk/id/eprint/7612 First version, 2009 (application/pdf)

Related works:
Journal Article: Real-time inflation forecast densities from ensemble Phillips curves (2011)
Working Paper: Real-time Inflation Forecast Densities from Ensemble Phillips Curves (2010)
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