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Forecasting UK Inflation: the Roles of Structural Breaks and Time Disaggregation

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  • Jennifer Castle
  • David Hendry

Abstract

Structural models` inflation forecasts are often inferior to those of naive devices. This chapter theoretically and empirically assesses this for UK annual and quarterly inflation, using the theoretical framework in Clements and Hendry (1998, 1999). Forecasts from equilibrium-correction mechanisms, built by automatic model selection, are compared to various robust devices. Forecast-error taxonomies for aggregated and time-disaggregated information reveal that the impacts of structural breaks are identical between these, so no gain results, helping interpret the empirical findings. Forecast failures in structural models are driven by their deterministic terms, confirming location shifts as a pernicious cause thereof, and explaining the success of robust devices.

Suggested Citation

  • Jennifer Castle & David Hendry, 2007. "Forecasting UK Inflation: the Roles of Structural Breaks and Time Disaggregation," Economics Series Working Papers 309, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:309
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    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Money growth & inflation
      by chris dillow in Stumbling and Mumbling on 2009-03-26 19:31:34

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    Cited by:

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    3. Thiago Carlomagno Carlo & Emerson Fernandes Marçal, 2016. "Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon," Applied Economics, Taylor & Francis Journals, vol. 48(50), pages 4846-4860, October.
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    5. Torre Cepeda Leonardo E. & Flores Segovia Miguel A., 2020. "Private Banking Credit and Economic Growth in Mexico: A State Level Panel Data Analysis 2005-2018," Working Papers 2020-17, Banco de México.
    6. Jacobs, Jan P.A.M. & Wallis, Kenneth F., 2010. "Cointegration, long-run structural modelling and weak exogeneity: Two models of the UK economy," Journal of Econometrics, Elsevier, vol. 158(1), pages 108-116, September.
    7. Christian Kascha & Francesco Ravazzolo, 2010. "Combining inflation density forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 231-250.
    8. Garcés Díaz Daniel, 2020. "On the Drivers of Inflation in Different Monetary Regimes," Working Papers 2020-16, Banco de México.

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    More about this item

    Keywords

    Inflation Forecasting; Structural Breaks; Robust Forecasts; Time-disaggregation; Foreign-error Taxonomies;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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