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Multi-sector inflation forecasting - quarterly models for South Africa

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  • Janine Aron
  • John Muellbauer

Abstract

Inflation is a far from homogeneous phenomenon, a fact often neglected in modeling consumer price inflation.� Using a novel methodology grounded in theory, the ten sub-components of the consumer price index (excluding mortgage interest rates), are modeled separately and forecast, four-quarters-ahead.� Equilibrium correction models in a rich multivariate form employ general and sectoral information, and take account of structural breaks and institutional changes.� Our methods allow for longer lags than conventionally considered in VARs, but in a parsimonious manner.� Sign priors are imposed on long-run effects and automatic model selection is used to select parsimonious models from more general ones.� The models throw light on sectoral sources of inflation, useful to monetary policy.� Data for 1979 to 2003 are used for model selection, and pseudo out of sample forecasting performance to the end of 2007 is examined.� Aggregating the weighted sub-component forecasts indicates gains are made over forecasting the overall index using these methods, and also substantial gains over forecasting using benchmark naive models.� To extend this work, including sectoral information such as an explicit treatment of tax policy, regulatory information and announced administred price rises, should further enhance these forecasting methods.

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Bibliographic Info

Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number WPS/2008-27.

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Date of creation: 01 Oct 2008
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Handle: RePEc:oxf:wpaper:wps/2008-27

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  1. Clements, Michael P & Hendry, David F, 1996. "Multi-step Estimation for Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 657-84, November.
  2. Janine Aron & John Muellbauer, 2004. "Construction of CPIX Data for Forecasting and Modelling in South Africa," Development and Comp Systems 0409056, EconWPA.
  3. Janine Aron & John Muellbauer & Coen Pretorius, 2004. "A Framework for Forecasting the Components of the Consumer Price Index: application to South Africa," Economics Series Working Papers WPS/2004-07, University of Oxford, Department of Economics.
  4. James H. Stock & Mark W. Watson, 2001. "Forecasting Output and Inflation: The Role of Asset Prices," NBER Working Papers 8180, National Bureau of Economic Research, Inc.
  5. J.W. Fedderke & E. Schaling, 2005. "Modelling Inflation In South Africa: A Multivariate Cointegration Analysis," South African Journal of Economics, Economic Society of South Africa, vol. 73(1), pages 79-92, 03.
  6. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  7. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
  8. Michael F. Bryan & Stephen G. Cecchetti, 1996. "Inflation and the Distribution of Price Changes," NBER Working Papers 5793, National Bureau of Economic Research, Inc.
  9. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  10. Christopher A. Sims, 1996. "Macroeconomics and Methodology," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 105-120, Winter.
  11. Janine Aron & John Muellbauer, 2008. "New methods for forecasting inflation and its sub-components: application to the USA," Economics Series Working Papers 406, University of Oxford, Department of Economics.
  12. 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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Cited by:
  1. Aron, Janine & Muellbauer, John, 2009. "Some Issues in Modeling and Forecasting Inflation in South Africa," CEPR Discussion Papers 7183, C.E.P.R. Discussion Papers.

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