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A Framework for Forecasting the Components of the Consumer Price Index: application to South Africa

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  • Janine Aron
  • John Muellbauer
  • Coen Pretorius

Abstract

Inflation is a far from homogeneous phenomenon, but this fact is ignored in most work on consumer price inflation. Using a novel methodology grounded in theory, the ten sub-components of the consumer price index (excluding mortgage interest rates, or CPIX) for South Africa are modeled separately and forecast, four quarters ahead. The method combines equilibrium correction models in a rich multivariate form with the use of stochastic trends estimated by the Kalman filter to capture structural breaks and institutional change. This research is of considerable practical use for monetary policy, allowing sectoral sources of inflation to be identified. Aggregating the forecasts of the components with appropriate weights from the overall index, potentially indicates the gains to be made in forecasting the idiosyncratic sectoral behaviour of prices, over forecasting the overall consumer price index.

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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/2004-07.

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Date of creation: 01 Mar 2004
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Handle: RePEc:oxf:wpaper:wps/2004-07

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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. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  3. Tobias, Justin & Zellner, Arnold, 2000. "A Note on Aggregation, Disaggregation and Forecasting Performance," Staff General Research Papers 12024, Iowa State University, Department of Economics.
  4. Christopher A. Sims, 1996. "Macroeconomics and Methodology," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 105-120, Winter.
  5. Aron, Janine & Muellbauer, John, 2000. "Personal and Corporate Saving in South Africa," CEPR Discussion Papers 2656, C.E.P.R. Discussion Papers.
  6. Weiss, Andrew A., 1991. "Multi-step estimation and forecasting in dynamic models," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 135-149.
  7. Johannes Fedderke & Chandana Kularatne & Martine Mariotti, 2007. "Mark-up Pricing in South African Industry," Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 16(1), pages 28-69, January.
  8. 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.
  9. Michael F. Bryan & Stephen G. Cecchetti, 1999. "Inflation And The Distribution Of Price Changes," The Review of Economics and Statistics, MIT Press, vol. 81(2), pages 188-196, May.
  10. 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.
  11. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
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Cited by:
  1. Janine Aron & John Muellbauer, 2004. "Construction of CPIX Data for Forecasting and Modelling in South Africa," CSAE Working Paper Series 2004-09, Centre for the Study of African Economies, University of Oxford.
  2. Janine Aron & John Muellbauer, 2008. "Multi-sector inflation forecasting - quarterly models for South Africa," CSAE Working Paper Series 2008-27, Centre for the Study of African Economies, University of Oxford.

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