Machine Learning And Causality The Impact Of Financial Crises On Growth
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Machine Learning and Causality: The Impact of Financial Crises on Growth
Author: Mr.Andrew J Tiffin
language: en
Publisher: International Monetary Fund
Release Date: 2019-11-01
Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.
Machine Learning and Causality: The Impact of Financial Crises on Growth
Author: Mr.Andrew J Tiffin
language: en
Publisher: International Monetary Fund
Release Date: 2019-11-01
Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.
The Impact of Gray-Listing on Capital Flows: An Analysis Using Machine Learning
Author: Mizuho Kida
language: en
Publisher: International Monetary Fund
Release Date: 2021-05-27
The Financial Action Task Force’s gray list publicly identifies countries with strategic deficiencies in their AML/CFT regimes (i.e., in their policies to prevent money laundering and the financing of terrorism). How much gray-listing affects a country’s capital flows is of interest to policy makers, investors, and the Fund. This paper estimates the magnitude of the effect using an inferential machine learning technique. It finds that gray-listing results in a large and statistically significant reduction in capital inflows.