Search Results

You are looking at 1 - 10 of 10 items for :

  • "Vulnerability Exercise approach using machine learning" x
Clear All
International Monetary Fund. Strategy, Policy, & Review Department
The IMF’s Vulnerability Exercise (VE) is a cross-country exercise that identifies country-specific near-term macroeconomic risks. As a key element of the Fund’s broader risk architecture, the VE is a bottom-up, multi-sectoral approach to risk assessments for all IMF member countries. The VE modeling toolkit is regularly updated in response to global economic developments and the latest modeling innovations. The new generation of VE models presented here leverages machine-learning algorithms. The models can better capture interactions between different parts of the economy and non-linear relationships that are not well measured in ”normal times.” The performance of machine-learning-based models is evaluated against more conventional models in a horse-race format. The paper also presents direct, transparent methods for communicating model results.
Hans Weisfeld, Mr. Irineu E de Carvalho Filho, Mr. Fabio Comelli, Rahul Giri, Klaus-Peter Hellwig, Chengyu Huang, Fei Liu, Mrs. Sandra V Lizarazo Ruiz, Alexis Meyer-Cirkel, and Mr. Andrea F Presbitero

Front Matter Page Strategy, Policy, and Review Department Foreword This paper was prepared in 2017–18 to provide options for strengthening risk analysis in low-income countries. Key features of the innovations it proposed, including the use of novel approaches to predictive modeling for low-income countries, were subsequently used to enhance the IMF’s risk analysis for countries at all income levels, which is presented in IMF: “How to Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning” (forthcoming). The present, earlier

International Monetary Fund. Strategy, Policy, & Review Department

Title Page TECHNICAL NOTES AND MANUALS How to Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning Approved by Sanjaya Panth Strategy, Policy, and Review Department INTERNATIONAL MONETARY FUND Title Page INTERNATIONAL MONETARY FUND How to Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning Approved by Sanjaya Panth Prepared by an interdepartmental team led by Kevin Wiseman, and comprising Pranav Gupta and Andrew Tiffin (AFR), Andrew Swiston (APD), Juliana Gamboa Arbelaez, Klaus

Yang Liu, Di Yang, and Mr. Yunhui Zhao

Risk: The Vulnerability Exercise Approach Using Machine Learning ,” Technical Notes and Manuals . Joseph , A . 2019 . “ Parametric Inference with Universal Function Approximators ,” Working Paper . Kohavi , R . 1995 . “ A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection ,” IJCAI’95: Proceedings of the 14th International Joint Conference on Artificial Intelligence , 2 , pp. 1137 – 1143 . Masini , R. , M.C. Medeiros , and E.F. Mendes . 2021 . “ Machine Learning Advances for Time Series Forecasting

Mr. Alberto Behar and Ramin Hassan

Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning. Technical Notes and Manuals. IMF International Monetary Fund ( 2021b ), 2021 External Sector Report: Divergent Recoveries and Global Imbalances . Jardim , E. , Long , M. C. , Plotnick , R. , van Inwegen , E. , Vigdor , J. , & Wething , H. ( 2020 ). Minimum wage increases and low-wage employment: Evidence from Seattle . American Economic Journal: Economic Policy . Jidoud , A. ( 2015 ). Remittances and macroeconomic volatility in African countries

Yang Liu, Di Yang, and Mr. Yunhui Zhao
Inflation has been rising during the pandemic against supply chain disruptions and a multi-year boom in global owner-occupied house prices. We present some stylized facts pointing to house prices as a leading indicator of headline inflation in the U.S. and eight other major economies with fast-rising house prices. We then apply machine learning methods to forecast inflation in two housing components (rent and owner-occupied housing cost) of the headline inflation and draw tentative inferences about inflationary impact. Our results suggest that for most of these countries, the housing components could have a relatively large and sustained contribution to headline inflation, as inflation is just starting to reflect the higher house prices. Methodologically, for the vast majority of countries we analyze, machine-learning models outperform the VAR model, suggesting some potential value for incorporating such models into inflation forecasting.
Hans Weisfeld, Mr. Irineu E de Carvalho Filho, Mr. Fabio Comelli, Rahul Giri, Klaus-Peter Hellwig, Chengyu Huang, Fei Liu, Mrs. Sandra V Lizarazo Ruiz, Alexis Meyer-Cirkel, and Mr. Andrea F Presbitero
In recent years, Fund staff has prepared cross-country analyses of macroeconomic vulnerabilities in low-income countries, focusing on the risk of sharp declines in economic growth and of debt distress. We discuss routes to broadening this focus by adding several macroeconomic and macrofinancial vulnerability concepts. The associated early warning systems draw on advances in predictive modeling.
Mr. Alberto Behar and Ramin Hassan
In terms of size, the net income balance (IB) is comparable to the trade balance (TB) for many countries. Yet the role of the IB in mitigating external vulnerabilities or complicating external adjustment remains underexplored. This paper studies the role of the IB in stabilizing or destabilizing the current account over the cycle and in crises. Our results show that, due to a negative correlation with the TB, the IB significantly dampens the time series volatility of the current account for most countries. However, the IB generally does not improve during crisis episodes, so current account adjustment occurs entirely through improvements in the TB. The paper also estimates IB semi-elasticities with respect to the exchange rate (ER). Semi-elasticities are small for most countries, so the IB is generally not a significant channel through which the ER stabilizes the current account, and trade-based semi-elasticities are, with some important exceptions, good proxies for current account semi-elasticities used in external sector assessments.
International Monetary Fund. Strategy, Policy, & and Review Department
A careful review has revealed significant scope to modernize and better align the MAC DSA with its objectives and the IMF’s lending framework. This note proposes replacing the current framework with a new methodology based on risk assessments at three different horizons. Extensive testing has shown that the proposed framework has much better predictive accuracy than the current one. In addition to predicting sovereign stress, the framework can be used to derive statements about debt stabilization under current policies and about debt sustainability.
International Monetary Fund. Strategy, Policy, & and Review Department

A careful review has revealed significant scope to modernize and better align the MAC DSA with its objectives and the IMF’s lending framework. This note proposes replacing the current framework with a new methodology based on risk assessments at three different horizons. Extensive testing has shown that the proposed framework has much better predictive accuracy than the current one. In addition to predicting sovereign stress, the framework can be used to derive statements about debt stabilization under current policies and about debt sustainability.