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Multi-sector inflation forecasting - quarterly models for South Africa

Author

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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-quartersahead. 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 naïve models. To extend this work, including sectoral information such as an explicit treatment of tax policy, regulatory information and announced administered price rises, should further enhance these forecasting methods.

Suggested Citation

  • 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.
  • Handle: RePEc:csa:wpaper:2008-27
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    File URL: http://www.csae.ox.ac.uk/materials/papers/2008-27text.pdf
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    References listed on IDEAS

    as
    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 657-684, November.
    2. 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. 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.
    2. Janine Aron & John n. j. Muellbauer & Coen Pretorius, 2009. "A Stochastic Estimation Framework For Components Of The South African Consumer Price Index," South African Journal of Economics, Economic Society of South Africa, vol. 77(2), pages 282-313, June.

    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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