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220928 ||| eng |
| 020 |
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|a 9781484380635
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| 100 |
1 |
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|a Jung, Jin-Kyu
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| 245 |
0 |
0 |
|a An Algorithmic Crystal Ball: Forecasts-based on Machine Learning
|c Jin-Kyu Jung, Manasa Patnam, Anna Ter-Martirosyan
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2018
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| 300 |
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|a 34 pages
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| 651 |
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4 |
|a United States
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| 653 |
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|a Machine learning
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| 653 |
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|a Technological Change: Choices and Consequences
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| 653 |
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|a Technology
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| 653 |
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|a Economic Forecasting
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| 653 |
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|a Intelligence (AI) & Semantics
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| 653 |
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|a Diffusion Processes
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| 653 |
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|a Economic forecasting
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| 653 |
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|a Neural Networks and Related Topics
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| 653 |
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|a Forecasting
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| 653 |
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|a Forecasting and Other Model Applications
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| 653 |
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|a Artificial intelligence
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| 700 |
1 |
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|a Patnam, Manasa
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| 700 |
1 |
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|a Ter-Martirosyan, Anna
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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| 490 |
0 |
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|a IMF Working Papers
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| 028 |
5 |
0 |
|a 10.5089/9781484380635.001
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| 856 |
4 |
0 |
|u https://elibrary.imf.org/view/journals/001/2018/230/001.2018.issue-230-en.xml?cid=46288-com-dsp-marc
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 330
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| 520 |
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|a 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
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