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Forecasting Inflation Using Summary Statistics of Survey Expectations: A Machine-Learning Approach

Author

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  • Bige Küçükefe

    (Namık Kemal University)

Abstract

This paper aims to produce more accurate short-term inflation forecasts based on surveys of expectations by employing machine-learning algorithms. By treating inflation forecasting as an estimation problem consisting of a label (inflation) and features (summary statistics of surveys of expectations data), we train a suite of machine-learning models, namely, Linear Regression, Bayesian Ridge Regression, Kernel Ridge Regression, Random Forests Regression, and Support Vector Machines, to forecast the consumer-price inflation (CPI) in Turkey. We employ the Time Series Cross Validation Procedure to ensure that the training data exclude forecast horizon data. Our results indicate that these machine-learning algorithms outperform the official forecasts of the Central Bank of Turkey (CBT) and a univariate model.

Suggested Citation

  • Bige Küçükefe, 2018. "Forecasting Inflation Using Summary Statistics of Survey Expectations: A Machine-Learning Approach," Ekonomi-tek - International Economics Journal, Turkish Economic Association, vol. 7(1), pages 1-16, January.
    • Handle: RePEc:tek:journl:v:7:y:2018:i:1:p:1-16
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    References listed on IDEAS

    as
    1. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    2. Magdalena Grothe & Aidan Meyler, 2018. "Inflation Forecasts: Are Market-Based and Survey-Based Measures Informative?," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 9(1), pages 171-188, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Machine learning; forecast evaluation; inflation forecasting; surveys of expectations; summary statistics;
    All these keywords.

    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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