Original Research

The role of oil prices in Philips curve modelling and forecasting of inflation

Ojo J. Adelakun, Harold Ngalawa
Journal of Economic and Financial Sciences | Vol 13, No 1 | a499 | DOI: https://doi.org/10.4102/jef.v13i1.499 | © 2020 Ojo J. Adelakun, Harold Ngalawa | This work is licensed under CC Attribution 4.0
Submitted: 20 July 2019 | Published: 25 June 2020

About the author(s)

Ojo J. Adelakun, Department of Economics, School of Accounting, Economics and Finance, College of Law and Management Studies, University of KwaZulu-Natal, Durban,, South Africa
Harold Ngalawa, Department of Economics, School of Accounting, Economics and Finance, College of Law and Management Studies, University of KwaZulu-Natal, Durban, South Africa


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Abstract

Orientation: The availability of an accurate and a reliable quantitative method for forecasting the behaviour of inflation is of importance, given the emphasis on price stability by central banks.

Research purpose: The conventional Phillips curve predictive model to explain the role of oil prices and associated implications in the forecasting of inflation using data from oil-exporting and oil-importing countries.

Motivation for the study: To determine whether augmenting the traditional demand-side Phillips curve with oil price supply-side shocks matters for the accuracy of predicting inflation using the Phillips curve. Study to investigate the role of oil prices in the Phillip curve accuracy to predict inflation from the perspective of oil exporting –oil importing dichotomy.

Research approach/design and method: We extend the conventional Phillips curve predictive model to explain the role of oil prices and associated implications in the forecasting of inflation using data from oil-exporting and oil-importing countries. The study demonstrates that the forecast performance of the traditional (demand-based) Phillips curve improves when it is augmented with oil prices.

Main findings: We also find that, contrary to previous findings in the literature, the augmented Phillips curve model, which incorporates oil prices as a supply-side factor, outperforms the random walk model.

Practical/managerial implications: The robustness of these findings is evident across different sub-sample periods, forecast horizons and individual oil-exporting and oil-importing countries under consideration.

Contribution/value-add: The comparison outcome further reaffirms that the augmented Phillips curve with changes in oil prices as a proxy for the supply-side factor is the preferred predictive model in both in-sample and out-of-sample forecast performance.


Keywords

inflation-forecasts; predictive model; Phillips curve; oil prices; RMSE; ARMSE; ARDL

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