An Algorithmic Crystal Ball: Forecasts-based on Machine Learning

Jin-Kyu Jung, Manasa Patnam and Anna Ter-Martirosyan

No 2018/230, IMF Working Papers from International Monetary Fund

Abstract: Forecasting macroeconomic variables is key to developing a view on a country's economic outlook. Most traditional forecasting models rely on fitting data to a pre-specified relationship between input and output variables, thereby assuming a specific functional and stochastic process underlying that process. We pursue a new approach to forecasting by employing a number of machine learning algorithms, a method that is data driven, and imposing limited restrictions on the nature of the true relationship between input and output variables. We apply the Elastic Net, SuperLearner, and Recurring Neural Network algorithms on macro data of seven, broadly representative, advanced and emerging economies and find that these algorithms can outperform traditional statistical models, thereby offering a relevant addition to the field of economic forecasting.

Keywords: WP; machine learning algorithm; real GDP; test data; time series; Machine learning; forecasts; forecast errors; machine learning model; WEO forecast; data set; benchmark WEO performance; Random Forest algorithm; generation process; decision tree algorithm; time-series data; WEO benchmark; Random Forest algorithms to nowcast GDP growth; training data; Artificial intelligence; Global (search for similar items in EconPapers)
Pages: 34
Date: 2018-11-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (26)

Downloads: (external link)
http://www.imf.org/external/pubs/cat/longres.aspx?sk=46288 (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:2018/230

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 ().