An Algorithmic Crystal Ball: Forecasts-based on Machine Learning

Author/Editor:

Jin-Kyu Jung ; Manasa Patnam ; Anna Ter-Martirosyan

Publication Date:

November 1, 2018

Electronic Access:

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Disclaimer: 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.

Summary:

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.

Series:

Working Paper No. 2018/230

Subject:

English

Publication Date:

November 1, 2018

ISBN/ISSN:

9781484380635/1018-5941

Stock No:

WPIEA2018230

Pages:

34

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