Comparing the New Keynesian Phillips Curve with Time Series Models to Forecast Inflation

Fabio Rumler and Maria Valderrama ()

Working Papers from Oesterreichische Nationalbank (Austrian Central Bank)

Abstract: The New Keynesian Phillips Curve, as a structural model of inflation dynamics, has mostly been used to explain past inflation developments, but has hardly been used for forecasting purposes. We propose a method of forecasting inflation based on the present-value formulation of the hybrid New Keynesian Phillips Curve. To evaluate the forecasting performance of this model we compare it with forecasts generated from time series models at different forecast horizons. As state-of-the-art time series models used in inflation forecasting we employ a Bayesian VAR, a traditional VAR and a simple autoregressive model. We find that the New Keynesian Phillips Curve delivers relatively more accurate forecasts compared to the other models for longer forecast horizons (more than 3 months) while they are outperformed by the time series models only for the very short forecast horizon. This is consistent with the finding in the literature that structural models are able to outperform time series models only for longer horizons.

Keywords: New Keynesian Phillips Curve; Inflation Forecasting; Forecast Evaluation; Bayesian VAR (search for similar items in EconPapers)
JEL-codes: C32 C53 E31 (search for similar items in EconPapers)
Pages: 37
Date: 2008-09-30
New Economics Papers: this item is included in nep-cba, nep-for and nep-mac
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Comparing the New Keynesian Phillips Curve with time series models to forecast inflation (2010)
Working Paper: Comparing the New Keynesian Phillips Curve with Time Series Models to Forecast Inflation (2007)
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