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

Bige Küçükefe ()
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Bige Küçükefe: Namık Kemal University

Ekonomi-tek - International Economics Journal, 2018, vol. 7, issue 1, 1-16

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.

Keywords: Machine learning; forecast evaluation; inflation forecasting; surveys of expectations; summary statistics (search for similar items in EconPapers)
JEL-codes: C82 E31 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:tek:journl:v:7:y:2018:i:1:p:1-16

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