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A Framework for Forecasting the Components of the Consumer Price

Author

Listed:
  • Janine Aron

    (Centre for the Study of African Economies)

  • John Muellbauer

    (Nuffield College, University of Oxford)

  • Coen Pretorius

    (South African Reserve Bank, Pretoria, South Africa)

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.

Suggested Citation

  • Janine Aron & John Muellbauer & Coen Pretorius, 2004. "A Framework for Forecasting the Components of the Consumer Price," Development and Comp Systems 0409054, EconWPA.
  • Handle: RePEc:wpa:wuwpdc:0409054 Note: Type of Document - pdf; pages: 63
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    File URL: http://econwpa.repec.org/eps/dev/papers/0409/0409054.pdf
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    References listed on IDEAS

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    1. Weiss, Andrew A., 1991. "Multi-step estimation and forecasting in dynamic models," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 135-149.
    2. Espasa, Antoni & Senra, Eva & Albacete, Rebeca, 2000. "Forecasting monetary union inflation: a disaggregated approach by countries and by sectors," DES - Working Papers. Statistics and Econometrics. WS 10143, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Zellner, Arnold & Chen, Bin, 2001. "Bayesian Modeling Of Economies And Data Requirements," Macroeconomic Dynamics, Cambridge University Press, vol. 5(05), pages 673-700, November.
    4. 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, March.
    5. Marvin J. Barth III & Valerie A. Ramey, 2002. "The Cost Channel of Monetary Transmission," NBER Chapters,in: NBER Macroeconomics Annual 2001, Volume 16, pages 199-256 National Bureau of Economic Research, Inc.
    6. Janine Aron & John Muellbauer, 2000. "Personal and Corporate Saving in South Africa," World Bank Economic Review, World Bank Group, vol. 14(3), pages 509-544, September.
    7. 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 657-684, November.
    8. Janine Aron & John Muellbauer, 2000. "Financial liberalisation, consumption and debt in South Africa," CSAE Working Paper Series 2000-22, Centre for the Study of African Economies, University of Oxford.
    9. Espasa, Antoni & Poncela, Pilar & Senra, Eva, 2002. "Forecasting monthly us consumer price indexes through a disaggregated I(2) analysis," DES - Working Papers. Statistics and Econometrics. WS ws020301, Universidad Carlos III de Madrid. Departamento de Estadística.
    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. Tobias, Justin & Zellner, Arnold, 2000. "A Note on Aggregation, Disaggregation and Forecasting Performance," Staff General Research Papers Archive 12024, Iowa State University, Department of Economics.
    12. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    13. Janine Aron & John Muellbauer, 2002. "Interest Rate Effects on Output: Evidence from a GDP Forecasting Model for South Africa," IMF Staff Papers, Palgrave Macmillan, vol. 49(Special i), pages 185-213.
    14. repec:cup:macdyn:v:5:y:2002:i:05:p:673-700_03 is not listed on IDEAS
    15. 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.
    16. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809, December.
    17. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    18. Harvey, A C & Jaeger, A, 1993. "Detrending, Stylized Facts and the Business Cycle," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(3), pages 231-247, July-Sept.
    19. Janine Aron & John Muellbauer, 2004. "Construction Of Cpix Data For Forecasting And Modelling In South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 72(5), pages 884-912, December.
    20. Chevalier, Judith A & Scharfstein, David S, 1996. "Capital-Market Imperfections and Countercyclical Markups: Theory and Evidence," American Economic Review, American Economic Association, vol. 86(4), pages 703-725, September.
    21. 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.
    22. 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.
    23. Espasa, Antoni & Llanos Matea, Maria de los, 1991. "Underlying inflation in the spanish economy: estimation and methodology," UC3M Working papers. Economics 2817, Universidad Carlos III de Madrid. Departamento de Economía.
    24. Espasa, Antoni & Albacete, Rebeca, 2004. "Econometric modelling for short-term inflation forecasting in the EMU," DES - Working Papers. Statistics and Econometrics. WS ws034309, Universidad Carlos III de Madrid. Departamento de Estadística.
    25. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    26. Christopher A. Sims, 1996. "Macroeconomics and Methodology," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 105-120, Winter.
    27. Janine Aron & John Muellbauer, 2000. "Inflation and output forecasts for South Africa: monetary transmission implications," CSAE Working Paper Series 2000-23, Centre for the Study of African Economies, University of Oxford.
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    Cited by:

    1. Jan Babecký & Fabrizio Coricelli & Roman Horváth, 2009. "Assessing Inflation Persistence: Micro Evidence on an Inflation Targeting Economy," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 59(2), pages 102-127, June.
    2. Janine Aron & John Muellbauer, 2004. "Construction Of Cpix Data For Forecasting And Modelling In South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 72(5), pages 884-912, December.
    3. Janine Aron & John Muellbauer, 2009. "Some Issues in Modeling and Forecasting Inflation in South Africa," CSAE Working Paper Series 2009-01, Centre for the Study of African Economies, University of Oxford.
    4. Ian Babetskii & Fabrizio Coricelli & Roman Horváth, 2007. "Measuring and Explaining Inflation Persistence: Disaggregate Evidence on the Czech Republic," Working Papers IES 2007/22, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Aug 2007.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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