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

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Listed author(s):
  • 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.

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Paper provided by Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia in its series Working Paper CRENoS with number 200307.

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Date of creation: 2003
Handle: RePEc:cns:cnscwp:200307
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Keywords: forecasting; inflation; threshold models; phillips curve;

Find related papers by 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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  1. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-883, November.
  2. Gali, Jordi & Gertler, Mark, 1999. "Inflation dynamics: A structural econometric analysis," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 195-222, October.
  3. Diebold, Francis X. & Nason, James A., 1990. "Nonparametric exchange rate prediction?," Journal of International Economics, Elsevier, vol. 28(3-4), pages 315-332, May.
  4. Robert J. Gordon, 1997. "The Time-Varying NAIRU and Its Implications for Economic Policy," Journal of Economic Perspectives, American Economic Association, vol. 11(1), pages 11-32, Winter.
  5. Douglas Staiger & James H. Stock & Mark W. Watson, 1997. "The NAIRU, Unemployment and Monetary Policy," Journal of Economic Perspectives, American Economic Association, vol. 11(1), pages 33-49, Winter.
  6. Clements, M.P. & Smith J., 1998. "Evaluating The Forecast of Densities of Linear and Non-Linear Models: Applications to Output Growth and Unemployment," The Warwick Economics Research Paper Series (TWERPS) 509, University of Warwick, Department of Economics.
  7. Ascari, Guido, 2000. "Optimising Agents, Staggered Wages and Persistence in the Real Effects of Money Shocks," Economic Journal, Royal Economic Society, vol. 110(465), pages 664-686, July.
  8. Francis X. Diebold & Anthony S. Tay & Kenneth F. Wallis, 1997. "Evaluating Density Forecasts of Inflation: The Survey of Professional Forecasters," NBER Working Papers 6228, National Bureau of Economic Research, Inc.
  9. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
  10. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
  11. Boero, Gianna & Marrocu, Emanuela, 2002. "The Performance of Non-linear Exchange Rate Models: A Forecasting Comparison," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(7), pages 513-542, November.
  12. Clements, M.P. & Smith, J., 1998. "Non-Linearities in Exchange Rates," The Warwick Economics Research Paper Series (TWERPS) 504, University of Warwick, Department of Economics.
  13. Flint Brayton & John M. Roberts & John C. Williams, 1999. "What's happened to the Phillips curve?," Finance and Economics Discussion Series 1999-49, Board of Governors of the Federal Reserve System (U.S.).
  14. Jonas D. M. Fisher & Chin Te Liu & Ruilin Zhou, 2002. "When can we forecast inflation?," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q I, pages 32-44.
  15. 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.
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Cited by:

  1. Afees A. Salisu & Ahaemefula 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.
  2. Afees A. Salisu & Kazeem Isah, 2017. "Predicting US CPI-Inflation in the presence of asymmetries, persistence, endogeneity, and conditional heteroscedasticity," Working Papers 026, Centre for Econometric and Allied Research, University of Ibadan.
  3. 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, FGV/EPGE - Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil), vol. 65(3), September.
  4. James H. Stock & Mark W. Watson, 2008. "Phillips curve inflation forecasts," Conference Series ; [Proceedings], Federal Reserve Bank of Boston, vol. 53.
  5. 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.
  6. 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.

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