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Forecasting inflation: a comparison of linear Phillips curve models and nonlinear time serie models

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  • G. Ascari
  • E. Marrocu

Abstract

The aim of this paper is to analyze the forecasting performance of alternative model for the US inflation rate over the period 1950.1-2002.7. NAIRU Phillips curve models forecasts are contrasted with those obtained by a special class of nonlinear time series models, the threshold autoregressive models. The forecast evaluation is conducted on point and density forecasts. The results show that overall the non linear specification are better able to capture the distributional features of the series, although in terms of MSFE the Phillips curve specification can yield noticeable forecasting gains for medium and long term horizons. Previous finding on the forecasting superiority of the simple naïve model are confuted.

Suggested Citation

  • G. Ascari & E. Marrocu, 2003. "Forecasting inflation: a comparison of linear Phillips curve models and nonlinear time serie models," Working Paper CRENoS 200307, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    • Handle: RePEc:cns:cnscwp:200307
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    References listed on IDEAS

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    Cited by:

    1. James H. Stock & Mark W. Watson, 2008. "Phillips curve inflation forecasts," Conference Series ; [Proceedings], Federal Reserve Bank of Boston.
    2. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017. "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 109-121, March.
    3. Afees A. Salisu & Kazeem O. Isah & Idris Ademuyiwa, 2017. "Testing for asymmetries in the predictive model for oil price-inflation nexus," Economics Bulletin, AccessEcon, vol. 37(3), pages 1797-1804.
    4. 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.
    5. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2017. "Improving the Predictive ability of oil for inflation: An ADL-MIDAS Approach," Working Papers 025, Centre for Econometric and Allied Research, University of Ibadan.
    6. repec:ipg:wpaper:2014-471 is not listed on IDEAS
    7. Arruda, Elano Ferreira & Ferreira, Roberto Tatiwa & Castelar, Ivan, 2011. "Modelos Lineares e Não Lineares da Curva de Phillips para Previsão da Taxa de Inflação no Brasil," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 65(3), September.
    8. Moses Tule & Afees Salisu & Charles Chiemeke, 2020. "Improving Nigeria’s Inflation Forecast with Oil Price: The Role of Estimators," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(1), pages 191-229, March.
    9. Patrick T. Kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2016. "Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation," Applied Economics, Taylor & Francis Journals, vol. 48(26), pages 2412-2427, June.
    10. Salisu, Afees A. & Isah, Kazeem O., 2018. "Predicting US inflation: Evidence from a new approach," Economic Modelling, Elsevier, vol. 71(C), pages 134-158.
    11. G. Marletto, 2006. "La politica dei trasporti come politica per l'innovazione: spunti da un approccio evolutivo," Working Paper CRENoS 200605, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    12. OA Carboni & G Medda, 2007. "Government Size and the Composition of Public Spending in a Neoclassical Growth Model," Working Paper CRENoS 200701, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    13. Johanna Posch & Fabio Rumler, 2015. "Semi‐Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 145-162, March.

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    More about this item

    Keywords

    forecasting; inflation; threshold models; phillips curve;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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