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Jin-Kyu Jung, Manasa Patnam, Anna Ter-Martirosyan, and Mr. Vikram Haksar

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.

International Monetary Fund. External Relations Dept.

IMF Managing Director Michel Camdessus expressed his formal condolences on the death of the King of Jordan. Following is the text of news brief 99/6, issued on February 7.

International Monetary Fund. Communications Department

Abstract

Address at the Bank of England Twentieth Anniversary Conference London, U.K. September 29, 2017 International Monetary Fund Managing Director Christine Lagarde delivered this address at the Bank of England conference, “Independence—20 Years On” in London, U.K., on September 29, 2017.

Mr. Anton Korinek, Mr. Martin Schindler, and Joseph Stiglitz