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Middle East and Central Asia > Qatar

You are looking at 1 - 10 of 11 items for :

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Cover IMF Working Papers
Predicting IMF-Supported Programs: A Machine Learning Approach
Tsendsuren Batsuuri
,
Shan He
,
Ruofei Hu
,
Jonathan Leslie
, and
Flora Lutz
This study applies state-of-the-art machine learning (ML) techniques to forecast IMF-supported programs, analyzes the ML prediction results relative to traditional econometric approaches, explores non-linear relationships among predictors indicative of IMF-supported programs, and evaluates model robustness with regard to different feature sets and time periods. ML models consistently outperform traditional methods in out-of-sample prediction of new IMF-supported arrangements with key predictors that align well with the literature and show consensus across different algorithms. The analysis underscores the importance of incorporating a variety of external, fiscal, real, and financial features as well as institutional factors like membership in regional financing arrangements. The findings also highlight the varying influence of data processing choices such as feature selection, sampling techniques, and missing data imputation on the performance of different ML models and therefore indicate the usefulness of a flexible, algorithm-tailored approach. Additionally, the results reveal that models that are most effective in near and medium-term predictions may tend to underperform over the long term, thus illustrating the need for regular updates or more stable – albeit potentially near-term suboptimal – models when frequent updates are impractical.
Keywords:
Early warning systems; IMF Lending; Machine Learning; IMF arrangement; ML model; machine learning approach; state-of-the-art machine learning; machine learning model; Emergency assistance; Data processing; Global financial crisis of 2008-2009; Global
Cover IMF Working Papers
Surrogate Data Models: Interpreting Large-scale Machine Learning Crisis Prediction Models
Mr. Jorge A Chan-Lau
,
Ruofei Hu
,
Maksym Ivanyna
,
Ritong Qu
, and
Cheng Zhong
Machine learning models are becoming increasingly important in the prediction of economic crises. The models, however, use datasets comprising a large number of predictors (features) which impairs model interpretability and their ability to provide adequate guidance in the design of crisis prevention and mitigation policies. This paper introduces surrogate data models as dimensionality reduction tools in large-scale crisis prediction models. The appropriateness of this approach is assessed by their application to large-scale crisis prediction models developed at the IMF. The results are consistent with economic intuition and validate the use of surrogates as interpretability tools.
Keywords:
Crisis prediction; machine learning; surrogates; explainable models; IMF ML crisis; ML crisis prediction; Annex I. IMF ML; surrogate data models; model interpretability; Early warning systems; Yield curve; Deposit rates; Global
Cover Technical Notes and Manuals
How to Assess Country Risk: The Vulnerability Exercise Approach Using Machine Learning
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.
Keywords:
Risk Assessment; Supervised Machine Learning; Prediction; Sudden Stop; Exchange Market Pressure; Fiscal Crisis; Debt; Financial Crisis; Economic Crisis; Economic Growth; VE modeling toolkit; ML technique; Vulnerability Exercise approach using machine learning; IMF's Vulnerability Exercise; ML tool; crisis risk indices; Early warning systems; Global financial crisis of 2008-2009; Sudden stops; Banking crises; Global
Cover IMF Working Papers
UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification
Mr. Jorge A Chan-Lau
We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses unsupervised representation learning and a novel mode contrastive autoencoder to group episodes into time-invariant non-overlapping clusters, each of which could be identified with a different regime. The likelihood that a country may experience an econmic crisis could be set equal to its cluster crisis frequency. Moreover, unFEAR could serve as a first step towards developing cluster-specific crisis prediction models tailored to each crisis regime.
Keywords:
clustering; unsupervised feature extraction; autoencoder; deep learning; biased label problem; crisis prediction; WP; crisis frequency; crisis observation; crisis risk; crisis data points; machine learning; Early warning systems; Global
Cover IMF Working Papers
Debt Is Not Free
Ms. Marialuz Moreno Badia
,
Mr. Paulo A Medas
,
Pranav Gupta
, and
Yuan Xiang
With public debt soaring across the world, a growing concern is whether current debt levels are a harbinger of fiscal crises, thereby restricting the policy space in a downturn. The empirical evidence to date is however inconclusive, and the true cost of debt may be overstated if interest rates remain low. To shed light into this debate, this paper re-examines the importance of public debt as a leading indicator of fiscal crises using machine learning techniques to account for complex interactions previously ignored in the literature. We find that public debt is the most important predictor of crises, showing strong non-linearities. Moreover, beyond certain debt levels, the likelihood of crises increases sharply regardless of the interest-growth differential. Our analysis also reveals that the interactions of public debt with inflation and external imbalances can be as important as debt levels. These results, while not necessarily implying causality, show governments should be wary of high public debt even when borrowing costs seem low.
Keywords:
WP; fiscal crisis; interest-growth differential; crisis threshold; crisis observation; probability of a crisis
Cover IMF Working Papers
Machine Learning and Causality: The Impact of Financial Crises on Growth
Mr. Andrew J Tiffin
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.
Keywords:
WP; machine-learning literature; instrumental-variables approach; treatment variable; confidence interval; ML technique; Supervised machine learning; causal inference; policy evaluation; counterfactual prediction; randomized experiments; treatment effects; banking crisis; financial crisis; B. machine learning; machine learning tool; machine-learning modification; RF algorithm; machine-learning model; Machine learning; Exchange rate flexibility; Global
Cover IMF Research Bulletin
IMF Research Perspectives: Spring/Summer 2018
IMF Research Perspective (formerly published as IMF Research Bulletin) is a new, redesigned online newsletter covering updates on IMF research. In the inaugural issue of the newsletter, Hites Ahir interviews Valeria Cerra; and they discuss the economic environment 10 years after the global financial crisis. Research Summaries cover the rise of populism; economic reform; labor and technology; big data; and the relationship between happiness and productivity. Sweta C. Saxena was the guest editor for this inaugural issue.
Keywords:
IRB; big data wave; populism; product market reform; reform agenda; rise of populism; big data innovation; Big data; Global financial crisis of 2008-2009; Personal income; Output gap; Global; Europe
Cover IMF Staff Country Reports
Qatar: Selected Issues
International Monetary Fund. Middle East and Central Asia Dept.
Qatar: Selected Issues
Keywords:
ISCR; CR; Qatar; risk; cost; investment; asset
Cover IMF Staff Country Reports
Qatar: Selected Issues
International Monetary Fund. Middle East and Central Asia Dept.
This Selected Issues paper assesses efficiency of Qatar public investment. It discusses the trends in public capital spending and the rationale for improving public investment efficiency. The paper outlines three alternative methods for analyzing efficiency, and presents the main results. The results suggest that the efficiency of Qatar public investment spending is broadly comparable to GCC peers, but could be improved further. It is also concluded that strengthening fiscal institutions, particularly with an integrated public investment management process and a medium-term fiscal policy framework, is the key for improving public investment efficiency in Qatar.
Keywords:
ISCR; CR; Qatar; risk; cost; investment; asset; Qatar economy; labor intensity; credit risk; crisis risk model; investment efficiency; asset price shock; price index; coverage ratio; construction cost; management process; implementation risk; risk premium; Public investment and public-private partnerships (PPP); Public investment spending; Early warning systems; Capital spending; Global
Cover IMF Working Papers
The Effects of the Global Crisison Islamic and Conventional Banks: A Comparative Study
Mr. Jemma Dridi
and
Maher Hasan
This paper examines the performance of Islamic banks (IBs) and conventional banks (CBs) during the recent global crisis by looking at the impact of the crisis on profitability, credit and asset growth, and external ratings in a group of countries where the two types of banks have significant market share. Our analysis suggests that IBs have been affected differently than CBs. Factors related to IBs‘ business model helped limit the adverse impact on profitability in 2008, while weaknesses in risk management practices in some IBs led to a larger decline in profitability in 2009 compared to CBs. IBs‘ credit and asset growth performed better than did that of CBs in 2008-09, contributing to financial and economic stability. External rating agencies‘ re-assessment of IBs‘ risk was generally more favorable.
Keywords:
WP; credit growth; market share; asset growth; investment portfolio; IB dummy; toxic asset; Islamic banks; conventional banks; financial stability; financial crisis; mean test; regression analysis; bank dummy; Islamic banks assets; Islamic banking; Islamic finance; Credit; Commercial banks; Global