Download This PaperDeus Ex Machina? A Framework for Macro Forecasting with Machine Learning
26 Pages Posted: 20 Apr 2020
Date Written: February 2020
Abstract
We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based models and use several machine learning models to predict real output growth with lower forecast errors than traditional models. By combining multiple machine learning models into ensembles, we lower forecast errors even further. We also identify measures of variable importance to help improve the transparency of machine learning-based forecasts. Applying the framework to Turkey reduces forecast errors by at least 30 percent relative to traditional models. The framework also better predicts economic volatility, suggesting that machine learning techniques could be an important part of the macro forecasting toolkit of many countries.
Keywords: Production growth, Capacity utilization, Economic growth, Stock markets, Emerging markets, Forecasts, Nowcasting, Machine learning, GDP growth, Cross-validation, Random Forest, Ensemble, Turkey., WP, forecast error, factor model, predictor, forecast, OLS
JEL Classification: C53, C45, E01, F16, C, Z13, O4
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