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Revisiting the forecasting accuracy of Phillips curve: The role of oil price

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  • Salisu, Afees A.
  • Ademuyiwa, Idris
  • Isah, Kazeem O.

Abstract

In this paper, we propose a revision to the traditional (demand side) Phillips curve to capture the supply (cost-push) side of inflation. We adopt the Westerlund and Narayan [WN] (2015) approach which accounts for persistence, endogeneity and conditional heteroscedasticity effects in the predictive regression model. In addition, following the approach of Salisu and Isah (2018), we extend the oil-based bivariate framework of WN (2015) to a multi-predictor set-up in order to augment the traditional Phillips curve-based inflation model with the proposed cost-push factor. Using the OECD countries, we demonstrate that the forecast performance of the traditional Phillips curve tends to improve when it is augmented with oil price both for the in-sample and out-of-sample forecasts. Contrary to the prominent findings in the literature, the augmented Phillips curve model outperforms the first order autoregressive model. Our results are robust to alternative measures of inflation rate and different forecast horizons.

Suggested Citation

  • Salisu, Afees A. & Ademuyiwa, Idris & Isah, Kazeem O., 2018. "Revisiting the forecasting accuracy of Phillips curve: The role of oil price," Energy Economics, Elsevier, vol. 70(C), pages 334-356.
  • Handle: RePEc:eee:eneeco:v:70:y:2018:i:c:p:334-356
    DOI: 10.1016/j.eneco.2018.01.018
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    1. Orphanides, Athanasios & van Norden, Simon, 2005. "The Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 583-601, June.
    2. James H. Stock & Mark W. Watson, 2008. "Phillips curve inflation forecasts," Conference Series ; [Proceedings], Federal Reserve Bank of Boston, vol. 53.
    3. Marie Diron & Benoît Mojon, 2008. "Are inflation targets good inflation forecasts?," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q II, pages 33-45.
    4. Bashar, Omar H.M.N. & Wadud, I.K.M. Mokhtarul & Ali Ahmed, Huson Joher, 2013. "Oil price uncertainty, monetary policy and the macroeconomy: The Canadian perspective," Economic Modelling, Elsevier, vol. 35(C), pages 249-259.
    5. Joakim Westerlund & Paresh Narayan, 2015. "Testing for Predictability in Conditionally Heteroskedastic Stock Returns," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 13(2), pages 342-375.
    6. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Is the Phillips Curve Alive and Well after All? Inflation Expectations and the Missing Disinflation," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 197-232, January.
    7. Stephen G. Cecchetti & Rita S. Chu & Charles Steindel, 2000. "The unreliability of inflation indicators," Current Issues in Economics and Finance, Federal Reserve Bank of New York, vol. 6(Apr).
    8. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    9. Kenneth Rogoff & Yu-chin Chen, 2002. "Commodity Currencies and Empirical Exchange Rate Puzzles," IMF Working Papers 02/27, International Monetary Fund.
    10. Bannigidadmath, Deepa & Narayan, Paresh Kumar, 2016. "Stock return predictability and determinants of predictability and profits," Emerging Markets Review, Elsevier, vol. 26(C), pages 153-173.
    11. Makin, Anthony J. & Narayan, Paresh Kumar & Narayan, Seema, 2014. "What expenditure does Anglosphere foreign borrowing fund?," Journal of International Money and Finance, Elsevier, vol. 40(C), pages 63-78.
    12. Westerlund, Joakim & Narayan, Paresh Kumar, 2012. "Does the choice of estimator matter when forecasting returns?," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2632-2640.
    13. Kiseok Lee & Shawn Ni & Ronald A. Ratti, 1995. "Oil Shocks and the Macroeconomy: The Role of Price Variability," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 39-56.
    14. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    15. Narayan, Paresh Kumar & Gupta, Rangan, 2015. "Has oil price predicted stock returns for over a century?," Energy Economics, Elsevier, vol. 48(C), pages 18-23.
    16. Stambaugh, Robert F., 1999. "Predictive regressions," Journal of Financial Economics, Elsevier, vol. 54(3), pages 375-421, December.
    17. Chen, Yu-chin & Rogoff, Kenneth, 2003. "Commodity currencies," Journal of International Economics, Elsevier, vol. 60(1), pages 133-160, May.
    18. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    19. repec:eee:intfin:v:52:y:2018:i:c:p:152-172 is not listed on IDEAS
    20. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2015. "Stock return forecasting: Some new evidence," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 38-51.
    21. Lutz Kilian & Robert J. Vigfusson, 2013. "Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 78-93, January.
    22. Bannigidadmath, Deepa & Narayan, Paresh Kumar, 2015. "Are Indian stock returns predictable?," Working Papers fe_2015_07, Deakin University, Department of Economics.
    23. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    24. repec:eee:ecmode:v:71:y:2018:i:c:p:134-158 is not listed on IDEAS
    25. Deepa & Paresh K Narayan, "undated". "Are Indian Stock Returns Predictable?," Financial Econometics Series 2015_07, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.
    26. Chen, Yu-chin & Turnovsky, Stephen J. & Zivot, Eric, 2014. "Forecasting inflation using commodity price aggregates," Journal of Econometrics, Elsevier, vol. 183(1), pages 117-134.
    27. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    28. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    29. repec:nbr:nberre:0126 is not listed on IDEAS
    30. Kilian, Lutz & Vigfusson, Robert J., 2011. "Nonlinearities In The Oil Price–Output Relationship," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 337-363, November.
    31. Neely, Christopher J., 2015. "How Much Do Oil Prices Affect Inflation?," Economic Synopses, Federal Reserve Bank of St. Louis, issue 10.
    32. Fischer, Stanley, 1977. "Long-Term Contracts, Rational Expectations, and the Optimal Money Supply Rule," Journal of Political Economy, University of Chicago Press, vol. 85(1), pages 191-205, February.
    33. repec:eee:energy:v:125:y:2017:i:c:p:97-106 is not listed on IDEAS
    34. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    35. Taylor, John B, 1980. "Aggregate Dynamics and Staggered Contracts," Journal of Political Economy, University of Chicago Press, vol. 88(1), pages 1-23, February.
    36. Marianna Riggi & Fabrizio Venditti, 2015. "Failing to Forecast Low Inflation and Phillips Curve Instability: A Euro-Area Perspective," International Finance, Wiley Blackwell, vol. 18(1), pages 47-68, March.
    37. Canova, Fabio, 2007. "G-7 Inflation Forecasts: Random Walk, Phillips Curve Or What Else?," Macroeconomic Dynamics, Cambridge University Press, vol. 11(01), pages 1-30, February.
    38. Robert E. Lucas, Jr. & Thomas J. Sargent, 1979. "After Keynesian macroeconomics," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Spr.
    39. Salisu, Afees A. & Isah, Kazeem O., 2018. "Predicting US inflation: Evidence from a new approach," Economic Modelling, Elsevier, vol. 71(C), pages 134-158.
    40. Gregory Mankiw, N. & Shapiro, Matthew D., 1986. "Do we reject too often? : Small sample properties of tests of rational expectations models," Economics Letters, Elsevier, vol. 20(2), pages 139-145.
    41. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    42. Lewellen, Jonathan, 2004. "Predicting returns with financial ratios," Journal of Financial Economics, Elsevier, vol. 74(2), pages 209-235, November.
    43. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    44. Dedeoğlu, Dinçer & Kaya, Hüseyin, 2014. "Pass-through of oil prices to domestic prices: Evidence from an oil-hungry but oil-poor emerging market," Economic Modelling, Elsevier, vol. 43(C), pages 67-74.
    45. Pal, Debdatta & Mitra, Subrata K., 2016. "Asymmetric oil product pricing in India: Evidence from a multiple threshold nonlinear ARDL model," Economic Modelling, Elsevier, vol. 59(C), pages 314-328.
    46. Salisu, Afees A. & Isah, Kazeem O., 2018. "Predicting US inflation: Evidence from a new approach," Economic Modelling, Elsevier, vol. 71(C), pages 134-158.
    47. Nelson, Charles R & Kim, Myung J, 1993. " Predictable Stock Returns: The Role of Small Sample Bias," Journal of Finance, American Finance Association, vol. 48(2), pages 641-661, June.
    48. Sharma, Susan Sunila, 2016. "Can consumer price index predict gold price returns?," Economic Modelling, Elsevier, vol. 55(C), pages 269-278.
    49. Narayan, Paresh Kumar & Bannigidadmath, Deepa, 2015. "Are Indian stock returns predictable?," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 506-531.
    50. Bec, Frédérique & De Gaye, Annabelle, 2016. "How do oil price forecast errors impact inflation forecast errors? An empirical analysis from US, French and UK inflation forecasts," Economic Modelling, Elsevier, vol. 53(C), pages 75-88.
    51. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, Elsevier.
    52. Narayan, Paresh Kumar & Sharma, Susan Sunila, 2014. "Firm return volatility and economic gains: The role of oil prices," Economic Modelling, Elsevier, vol. 38(C), pages 142-151.
    53. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Win, pages 2-11.
    54. Sam Olofin & Afees A. Salisu, 2017. "Modelling oil price-inflation nexus: The role of asymmetries and structural breaks," Working Papers 020, Centre for Econometric and Allied Research, University of Ibadan.
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    Citations

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    Cited by:

    1. Afees A. Salisu & Umar B. Ndako & Idris Adediran, 2018. "Forecasting GDP of OPEC: The role of oil price," Working Papers 044, Centre for Econometric and Allied Research, University of Ibadan.
    2. Afees A. Salisu & Umar B. Ndako, 2017. "A new look at the stock price-exchange rate nexus," Working Papers 031, Centre for Econometric and Allied Research, University of Ibadan.
    3. Afees A. Salisu & Oluwatomisinn Oyewole & Ismail O. Fasanya, 2017. "Modelling Return and Volatility Spillovers in Global Foreign Exchange Markets," Working Papers 030, Centre for Econometric and Allied Research, University of Ibadan.
    4. Kazeem Isah & Ibrahim D. Raheem, 2018. "The Hidden Predictive Power of Cryptocurrencies: Evidence from US Stock Market," Working Papers 056, Centre for Econometric and Allied Research, University of Ibadan.
    5. Afees A. Salisu & Wasiu Adekunle & Zachariah Emmanuel & Wasiu A. Alimi, 2018. "Predicting exchange rate with commodity prices: The role of structural breaks and asymmetries," Working Papers 055, Centre for Econometric and Allied Research, University of Ibadan.
    6. Afees A. Salisu & Kazeem Isah & Lateef O. Akanni, 2018. "Predicting the stock prices of G7 countries with Bitcoin prices," Working Papers 054, Centre for Econometric and Allied Research, University of Ibadan.
    7. Moses Tule & Afees A. Salisu & Charles Chimeke, 2018. "You are what you eat: The role of oil price in Nigeria inflation forecast," Working Papers 040, Centre for Econometric and Allied Research, University of Ibadan.
    8. Afees A. Salisu & Ahamuefula Ephraim Ogbonna & Paul Adeoye Omosebi, 2018. "Does the choice of estimator matter for forecasting? A revisit," Working Papers 053, Centre for Econometric and Allied Research, University of Ibadan.

    More about this item

    Keywords

    OECD countries; Phillips curve; Oil price; Inflation forecasts; Forecast evaluation;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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