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New methods for forecasting inflation, applied to the US

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  • Aron, Janine
  • Muellbauer, John

Abstract

Models for the twelve-month-ahead US rate of inflation, measured by the chain weighted consumer expenditure deflator, are estimated for 1974-99 and subsequent pseudo out-of-sample forecasting performance is examined. Alternative forecasting approaches for different information sets are compared with benchmark univariate autoregressive models, and substantial out-performance is demonstrated. Three key ingredients to the out-performance are: including equilibrium correction terms in relative prices; introducing non-linearities to proxy state dependence in the inflation process; and replacing the information criterion, commonly used in VARs to select lag length, with a ‘parsimonious longer lags’ (PLL) parameterisation. Forecast pooling or averaging also improves forecast performance.

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Bibliographic Info

Paper provided by C.E.P.R. Discussion Papers in its series CEPR Discussion Papers with number 7877.

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Date of creation: Jun 2010
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Handle: RePEc:cpr:ceprdp:7877

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Keywords: Error Correction Models; Evaluating Forecasts; Model Selection; Multivariate Time Series;

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References

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  1. 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.
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  3. Jeremy Rudd & Karl Whelan, 2007. "Modeling Inflation Dynamics: A Critical Review of Recent Research," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 155-170, 02.
  4. Filippo Altissimo & Laurent Bilke & Andrew Levin & Thomas Mathä & Benoit Mojon, 2006. "Sectoral and Aggregate Inflation Dynamics in the Euro Area," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 585-593, 04-05.
  5. Weiss, Andrew A., 1991. "Multi-step estimation and forecasting in dynamic models," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 135-149.
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  8. Ignazio Angeloni & Luc Aucremanne & Michael Ehrmann & Jordi Galí & Andrew Levin & Frank Smets, 2006. "New Evidence on Inflation Persistence and Price Stickiness in the Euro Area: Implications for Macro Modeling," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 562-574, 04-05.
  9. John V. Duca & Carl M. Campbell, III, 2007. "The impact of evolving labor practices and demographics on U.S. inflation and unemployment," Working Papers 0702, Federal Reserve Bank of Dallas.
  10. Sheshinski, Eytan & Weiss, Yoram, 1977. "Inflation and Costs of Price Adjustment," Review of Economic Studies, Wiley Blackwell, vol. 44(2), pages 287-303, June.
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  12. Mavroeidis, Sophocles, 2005. "Identification Issues in Forward-Looking Models Estimated by GMM, with an Application to the Phillips Curve," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 421-48, June.
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Cited by:
  1. James H. Stock & Mark W. Watson, 2010. "Modeling Inflation After the Crisis," NBER Working Papers 16488, National Bureau of Economic Research, Inc.
  2. Aron, Janine & Muellbauer, John, 2010. "Does aggregating forecasts by CPI component improve inflation forecast accuracy in South Africa?," CEPR Discussion Papers 7895, C.E.P.R. Discussion Papers.
  3. Amstad, Marlene & Potter, Simon M. & Rich, Robert W., 2014. "The FRBNY staff underlying inflation gauge: UIG," Staff Reports 672, Federal Reserve Bank of New York.
  4. Aron, Janine & Muellbauer, John, 2012. "Improving forecasting in an emerging economy, South Africa: Changing trends, long run restrictions and disaggregation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 456-476.

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