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Forecasting UK inflation bottom up

Author

Listed:
  • Joseph, Andreas

    (Bank of England)

  • Kalamara, Eleni

    (King’s College London)

  • Kapetanios, George

    (King’s College London)

  • Potjagailo, Galina

    (Bank of England)

  • Chakraborty, Chiranjit

    (Bank of England)

Abstract

We forecast CPI inflation in the United Kingdom up to one year ahead using a large set of monthly disaggregated CPI item series and a wide set of forecasting tools, including dimensionality reduction techniques, shrinkage methods, and non-linear machine learning models. We find that over the full sample period 2002–21, the Ridge regression combined with CPI item series yields substantial improvement against an autoregressive benchmark at the six-month horizon, whereas the benchmark is hard to beat with other models and for other horizons. However, when considering periods of time where aggregate CPI inflation measures exhibit changes in momentum (rising or falling) or tail values, a wide range of models leads to substantial significant relative forecast gains. Exploiting CPI items through shrinkage methods yields strongest gains at horizons of 6–12 months when headline and core inflation measures are rising or falling. At shorter horizons and when inflation is rising, machine learning tools combined with CPI items and macroeconomic indicators are more useful. We also provide a model-agnostic approach based on model Shapley value decompositions to interpret and communicate signals from groups of items according to interpretable CPI categories.

Suggested Citation

  • Joseph, Andreas & Kalamara, Eleni & Kapetanios, George & Potjagailo, Galina & Chakraborty, Chiranjit, 2021. "Forecasting UK inflation bottom up," Bank of England working papers 915, Bank of England, revised 27 Sep 2022.
  • Handle: RePEc:boe:boeewp:0915
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    2. Philippe Goulet Coulombe & Karin Klieber & Christophe Barrette & Maximilian Goebel, 2024. "Maximally Forward-Looking Core Inflation," Papers 2404.05209, arXiv.org.
    3. Simionescu, Mihaela, 2022. "Econometrics of sentiments- sentometrics and machine learning: The improvement of inflation predictions in Romania using sentiment analysis," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    4. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    5. Gabe de Bondt & Arne Gieseck & Pablo Herrero & Zivile Zekaite, 2021. "Euro Area Income and Wealth Effects: Aggregation Issues," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1454-1474, December.
    6. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    7. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.

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    More about this item

    Keywords

    Inflation; forecasting; machine learning; state space models; CPI disaggregated data; Shapley values;
    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
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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