New analysis from researchers at the University of Toronto and the IMF suggests a framework to nowcast (and forecast) economic variables with machine learning techniques.

The findings published in a report titled 'Deus ex Machina? A Framework for Macro Forecasting with Machine Learning' 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

"Machine learning (ML) methods present an alternative to traditional forecasting techniques. ML models can outperform traditional forecasting methods because they emphasize out-of-sample (rather than in-sample) performance and better handle nonlinear interactions among a large number of predictors," Marijn A. Bolhuis, lead researcher on the IMF report, notes.

The research explains 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.

"ML methods are specifically designed to learn complex relationships from past data while resisting the tendency of traditional methods to over-extrapolate historical relationships into the future. Indeed, a literature is beginning to emerge which suggests that ML methods often outperform traditional linear regression-based methods in terms of accuracy and robustness," Bolhuis adds.

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.

"Techniques based on OLS struggle to overcome several issues,
including collinearity, dimensionality, predictor relevance, and nonlinearity. As a result, even state-of-the art forecasting models often result in large forecast errors, especially when the variable to be predicted is volatile, such as output growth in many emerging market and developing economies. ML models can outperform traditional forecasting methods because they emphasize out-of-sample (rather than in-sample) performance and better handle nonlinear interactions among a large number of predictors. ML methods are specifically designed to learn complex relationships from past data while resisting the tendency of traditional methods to over-extrapolate historical relationships into the future," Bolhuis concludes.


Read this working paper in full: https://www.imf.org/en/Publications/WP/Issues/2020/02/28/Deus-ex-Machina-A-Framework-for-Macro-Forecasting-with-Machine-Learning-49094

IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. For a full list of IMF Publications visit: https://www.imf.org/en/Publications

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