Dergiler / Ekonomi-tek / 2018 / Cilt: 7 - Sayı: 1
Forecasting Inflation Using Summary Statistics of Survey Expectations: A Machine-Learning Approach
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- Ekonomi-tek
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- 1–16
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Özet
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.
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.