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Can urban coffee consumption help predict US inflation?

Salisu, Afees A; Swaray, Raymond; Adediran, Idris A

Authors

Afees A Salisu

Dr Raymond Swaray R.Swaray@hull.ac.uk
Senior Lecturer (Associate Professor) in Economics

Idris A Adediran



Abstract

Motivated by the importance of coffee to Americans and the significance of the coffee subsector to the US economy, we pursue three notable innovations. First, we augment the traditional Phillips curve model with the coffee price as a predictor, and show that the resulting model outperforms the traditional variant in both in-sample and out-of-sample predictability of US inflation. Second, we demonstrate the need to account for the inherent statistical features of predictors such as persistence, endogeneity and conditional heteroskedasticity effects when dealing with US inflation. Consequently, we offer robust illustrations to show that the choice of estimator matters for improved US inflation forecasts. Third, the proposed augmented Phillips curve also outperforms time-series models such as ARIMA and ARFIMA for both in-sample and out-of-sample forecasts. Our results show that augmenting the traditional Phillips curve with the urban coffee price will produce better forecast results for US inflation only when the statistical effects are captured in the estimation process. Our results are robust to alternative measures of inflation, different data frequencies, higher-order moments, multiple data samples and multiple forecast horizons.

Journal Article Type Article
Publication Date 2019-11
Journal Journal of forecasting
Print ISSN 0277-6693
Electronic ISSN 1099-131X
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 38
Issue 7
Pages 649-668
APA6 Citation Salisu, A. A., Adediran, I. A., Salisu, A. A., Swaray, R., & Adediran, I. A. (2019). Can urban coffee consumption help predict US inflation?. Journal of forecasting, 38(7), 649-668. https://doi.org/10.1002/for.2589
DOI https://doi.org/10.1002/for.2589
Keywords USA; Phillips curve; Coffee price; Inflation forecasts; Forecast evaluation
Publisher URL https://onlinelibrary.wiley.com/doi/abs/10.1002/for.2589
Additional Information Received: 2018-04-23; Accepted: 2019-03-08; Published: 2019-04-25

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This file is under embargo until Apr 26, 2021 due to copyright reasons.

Contact R.Swaray@hull.ac.uk to request a copy for personal use.




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