Predicting Fiscal Crises: A Machine Learning Approach
Predicting Fiscal Crises: A Machine Learning Approach

Author: Klaus-Peter Hellwig

Publisher: International Monetary Fund

Published: 2021-05-27

Total Pages: 66

ISBN-13: 1513573586

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In this paper I assess the ability of econometric and machine learning techniques to predict fiscal crises out of sample. I show that the econometric approaches used in many policy applications cannot outperform a simple heuristic rule of thumb. Machine learning techniques (elastic net, random forest, gradient boosted trees) deliver significant improvements in accuracy. Performance of machine learning techniques improves further, particularly for developing countries, when I expand the set of potential predictors and make use of algorithmic selection techniques instead of relying on a small set of variables deemed important by the literature. There is considerable agreement across learning algorithms in the set of selected predictors: Results confirm the importance of external sector stock and flow variables found in the literature but also point to demographics and the quality of governance as important predictors of fiscal crises. Fiscal variables appear to have less predictive value, and public debt matters only to the extent that it is owed to external creditors.

Predicting Fiscal Crises
Predicting Fiscal Crises

Author: Ms.Svetlana Cerovic

Publisher: International Monetary Fund

Published: 2018-08-03

Total Pages: 42

ISBN-13: 1484372913

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This paper identifies leading indicators of fiscal crises based on a large sample of countries at different stages of development over 1970-2015. Our results are robust to different methodologies and sample periods. Previous literature on early warning sistems (EWS) for fiscal crises is scarce and based on small samples of advanced and emerging markets, raising doubts about the robustness of the results. Using a larger sample, our analysis shows that both nonfiscal (external and internal imbalances) and fiscal variables help predict crises among advanced and emerging economies. Our models performed well in out-of-sample forecasting and in predicting the most recent crises, a weakness of EWS in general. We also build EWS for low income countries, which had been overlooked in the literature.

Predicting Fiscal Crises
Predicting Fiscal Crises

Author: Svetlana Cerovic

Publisher:

Published: 2018

Total Pages: 0

ISBN-13:

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This paper identifies leading indicators of fiscal crises based on a large sample of countries at different stages of development over 1970-2015. Our results are robust to different methodologies and sample periods. Previous literature on early warning sistems (EWS) for fiscal crises is scarce and based on small samples of advanced and emerging markets, raising doubts about the robustness of the results. Using a larger sample, our analysis shows that both nonfiscal (external and internal imbalances) and fiscal variables help predict crises among advanced and emerging economies. Our models performed well in out-of-sample forecasting and in predicting the most recent crises, a weakness of EWS in general. We also build EWS for low income countries, which had been overlooked in the literature.

Financial Crises Explanations, Types, and Implications
Financial Crises Explanations, Types, and Implications

Author: Mr.Stijn Claessens

Publisher: International Monetary Fund

Published: 2013-01-30

Total Pages: 66

ISBN-13: 1475561008

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This paper reviews the literature on financial crises focusing on three specific aspects. First, what are the main factors explaining financial crises? Since many theories on the sources of financial crises highlight the importance of sharp fluctuations in asset and credit markets, the paper briefly reviews theoretical and empirical studies on developments in these markets around financial crises. Second, what are the major types of financial crises? The paper focuses on the main theoretical and empirical explanations of four types of financial crises—currency crises, sudden stops, debt crises, and banking crises—and presents a survey of the literature that attempts to identify these episodes. Third, what are the real and financial sector implications of crises? The paper briefly reviews the short- and medium-run implications of crises for the real economy and financial sector. It concludes with a summary of the main lessons from the literature and future research directions.

Machine Learning and Causality: The Impact of Financial Crises on Growth
Machine Learning and Causality: The Impact of Financial Crises on Growth

Author: Mr.Andrew J Tiffin

Publisher: International Monetary Fund

Published: 2019-11-01

Total Pages: 30

ISBN-13: 1513519514

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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.

Predicting Sovereign Debt Crises
Predicting Sovereign Debt Crises

Author: Paolo Manasse

Publisher: International Monetary Fund

Published: 2003-11-01

Total Pages: 42

ISBN-13: 1451875258

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We develop an early-warning model of sovereign debt crises. A country is defined to be in a debt crisis if it is classified as being in default by Standard & Poor's, or if it has access to nonconcessional IMF financing in excess of 100 percent of quota. By means of logit and binary recursive tree analysis, we identify macroeconomic variables reflecting solvency and liquidity factors that predict a debt-crisis episode one year in advance. The logit model predicts 74 percent of all crises entries while sending few false alarms, and the recursive tree 89 percent while sending more false alarms.

The Feasibility of Predicting Financial Crises using Machine Learning
The Feasibility of Predicting Financial Crises using Machine Learning

Author: Julia Markhovski

Publisher: GRIN Verlag

Published: 2024-03-26

Total Pages: 114

ISBN-13: 3389003649

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Bachelor Thesis from the year 2024 in the subject Computer Science - Commercial Information Technology, grade: 1.0, Frankfurt School of Finance & Management, language: English, abstract: In a world characterized by increasingly complex financial markets, the prediction of financial crises is a constant challenge. This bachelor thesis investigates the use of machine learning, in particular regression algorithms, to analyze and predict financial crises based on macroeconomic data. By building six different regression models and optimizing them using cross-validation and GridSearch, the feasibility of using these technologies for accurate predictions is discussed. Although traditional models show limited effectiveness, the integration of machine learning, especially kNN algorithms, reveals significant potential for improving prediction accuracy. The paper highlights the importance of classification algorithms and provides crucial insights for application in real-world scenarios to provide valuable tools for policy and business decision makers.

The Challenge of Predicting Economic Crises
The Challenge of Predicting Economic Crises

Author: Ms.Catherine A. Pattillo

Publisher: International Monetary Fund

Published: 2000-09-11

Total Pages: 22

ISBN-13: 9781557758842

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The integration of financial markets around the world over the past decade has posed new challenges for policymakers. The speed with which money can be switched in and out of currencies and countries has increased with the efficiency of global communications, considerably shortening the time policymakers have to respond to emerging crises. This pamphlet takes alook at attempts by economists to predict crises by developing early warning systems to signal when trouble may be brewing in currency markets and banking systems.

Does Financial Connectedness Predict Crises?
Does Financial Connectedness Predict Crises?

Author: Ms.Camelia Minoiu

Publisher: International Monetary Fund

Published: 2013-12-24

Total Pages: 44

ISBN-13: 1475554257

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The global financial crisis has reignited interest in models of crisis prediction. It has also raised the question whether financial connectedness - a possible source of systemic risk - can serve as an early warning indicator of crises. In this paper we examine the ability of connectedness in the global network of financial linkages to predict systemic banking crises. Our results indicate that increases in a country's financial interconnectedness and decreases in its neighbors' connectedness are associated with a higher probability of banking crises after controlling for macroeconomic fundamentals.