An Algorithmic Crystal Ball: Forecasts-Based on Machine Learning

35 Pages Posted: 10 Dec 2018

See all articles by Jin-Kyu Jung

Jin-Kyu Jung

WHU - Otto Beisheim School of Management

Manasa Patnam

International Monetary Fund (IMF)

Anna Ter-Martirosyan

International Monetary Fund (IMF)

Date Written: November 2018

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 inputand output variables, thereby assuming a specific functional and stochastic process underlying thatprocess. We pursue a new approach to forecasting by employing a number of machine learningalgorithms, a method that is data driven, and imposing limited restrictions on the nature of the truerelationship between input and output variables. We apply the Elastic Net, SuperLearner, andRecurring Neural Network algorithms on macro data of seven, broadly representative, advanced andemerging economies and find that these algorithms can outperform traditional statistical models,thereby offering a relevant addition to the field of economic forecasting.

Keywords: Information technology, Economic forecasting, Forecasting models, Machine learning, forecasts, forecast errors, Forecasting and Other Model Applications, Neural Networks and Related Topics

JEL Classification: C53, C45

Suggested Citation

Jung, Jin-Kyu and Patnam, Manasa and Ter-Martirosyan, Anna, An Algorithmic Crystal Ball: Forecasts-Based on Machine Learning (November 2018). IMF Working Paper No. 18/230, Available at SSRN: https://ssrn.com/abstract=3297651

Jin-Kyu Jung (Contact Author)

WHU - Otto Beisheim School of Management ( email )

Burgplatz 2
Vallendar, 56179
Germany

Manasa Patnam

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

Anna Ter-Martirosyan

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

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