Deus ex Machina? A Framework for Macro Forecasting with Machine Learning

Marijn Bolhuis and Brett Rayner

No 2020/045, IMF Working Papers from International Monetary Fund

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: WP; ML model; ML method; RF algorithm; SVM regression; forecasting method; forecast error; Factor models; Machine learning; Global; Forecasts; Nowcasting; GDP growth; Cross-validation; Random Forest; Ensemble; Turkey (search for similar items in EconPapers)
Pages: 25
Date: 2020-02-28
New Economics Papers: this item is included in nep-ara, nep-big, nep-cmp and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.imf.org/external/pubs/cat/longres.aspx?sk=49094 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:imf:imfwpa:2020/045

Ordering information: This working paper can be ordered from
http://www.imf.org/external/pubs/pubs/ord_info.htm

Access Statistics for this paper

More papers in IMF Working Papers from International Monetary Fund International Monetary Fund, Washington, DC USA. Contact information at EDIRC.
Bibliographic data for series maintained by Akshay Modi ().