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Nowcasting US inflation using a MIDAS augmented Phillips curve

Nowcasting US inflation using a MIDAS augmented Phillips curve We present a mixed-frequency model for real-time monitoring of US inflation. Our approach relies on a mixed-data sampling (MIDAS) augmented Phillips curve with daily oil prices for nowcasting inflation. In line with the literature we find that performances of inflation nowcasting models rely on two key elements: the inclusion of high-frequency oil prices and a rollingwindow framework. Our approach succeeds in providing a policy-oriented tool for monitoring inflation in real-time. Keywords: inflation; nowcasting; MIDAS; mixed-data sampling; oil prices. Reference to this paper should be made as follows: Marsilli, C. (2017) `Nowcasting US inflation using a MIDAS augmented Phillips curve', Int. J. Computational Economics and Econometrics, Vol. 7, Nos. 1/2, pp.64­77. Biographical notes: Clément Marsilli is a Economist at the International Macroeconomics Division of Banque de France. 1 Introduction Central banks of advanced economies aim at maintaining price stability. Real-time monitoring of economic activity and price evolutions is essential for inflation targeting but is also regarded as a real challenge for econometricians. In fact the economic literature has reached a broad consensus: "forecasting inflation is hard".1 In the context of inflation modelling, Phillips curve is an important workhorse. Since the original work of Fisher (1926) and Phillips (1958), various http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Computational Economics and Econometrics Inderscience Publishers

Nowcasting US inflation using a MIDAS augmented Phillips curve

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Publisher
Inderscience Publishers
Copyright
Copyright © 2017 Inderscience Enterprises Ltd.
ISSN
1757-1170
eISSN
1757-1189
DOI
10.1504/IJCEE.2017.080649
Publisher site
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Abstract

We present a mixed-frequency model for real-time monitoring of US inflation. Our approach relies on a mixed-data sampling (MIDAS) augmented Phillips curve with daily oil prices for nowcasting inflation. In line with the literature we find that performances of inflation nowcasting models rely on two key elements: the inclusion of high-frequency oil prices and a rollingwindow framework. Our approach succeeds in providing a policy-oriented tool for monitoring inflation in real-time. Keywords: inflation; nowcasting; MIDAS; mixed-data sampling; oil prices. Reference to this paper should be made as follows: Marsilli, C. (2017) `Nowcasting US inflation using a MIDAS augmented Phillips curve', Int. J. Computational Economics and Econometrics, Vol. 7, Nos. 1/2, pp.64­77. Biographical notes: Clément Marsilli is a Economist at the International Macroeconomics Division of Banque de France. 1 Introduction Central banks of advanced economies aim at maintaining price stability. Real-time monitoring of economic activity and price evolutions is essential for inflation targeting but is also regarded as a real challenge for econometricians. In fact the economic literature has reached a broad consensus: "forecasting inflation is hard".1 In the context of inflation modelling, Phillips curve is an important workhorse. Since the original work of Fisher (1926) and Phillips (1958), various

Journal

International Journal of Computational Economics and Econometrics – Inderscience Publishers

Published: Jan 1, 2017

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