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Middle East and Central Asia > Qatar

You are looking at 1 - 10 of 41 items for :

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Cover IMF Working Papers
Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning
Yang Liu
,
Ran Pan
, and
Rui Xu
Forecasting inflation has become a major challenge for central banks since 2020, due to supply chain disruptions and economic uncertainty post-pandemic. Machine learning models can improve forecasting performance by incorporating a wider range of variables, allowing for non-linear relationships, and focusing on out-of-sample performance. In this paper, we apply machine learning (ML) models to forecast near-term core inflation in Japan post-pandemic. Japan is a challenging case, because inflation had been muted until 2022 and has now risen to a level not seen in four decades. Four machine learning models are applied to a large set of predictors alongside two benchmark models. For 2023, the two penalized regression models systematically outperform the benchmark models, with LASSO providing the most accurate forecast. Useful predictors of inflation post-2022 include household inflation expectations, inbound tourism, exchange rates, and the output gap.
Keywords:
Core inflation; forecasting; machine learning models; LASSO; Japan; household inflation expectation; forecasting inflation; machine learning method; Annex I. machine learning; enhanced inflation; Inflation; Machine learning; Econometric models; Output gap
Cover IMF Staff Country Reports
Kingdom of Lesotho: Selected Issues
International Monetary Fund. African Dept.
This Selected Issues paper delves into few applications of machine learning (ML), with a particular application to economic forecasts in Lesotho. Amid delayed and often revised gross domestic product data, this paper explores the potential of ML to provide real-time insights into growth and inflation trends, crucial for informed policymaking. By leveraging nontraditional data and employing a variety of ML models, the paper presents a comprehensive analysis of current economic activity, evaluates the accuracy of standard statistical measures, and forecasts future inflation trends. The findings underscore the efficacy of ML in reducing prediction errors and highlight the significant role of alternative data in circumventing the limitations posed by traditional economic indicators. This paper contributes to the broader debate on the application of advanced computational techniques in economic forecasting, offering valuable insights for policymakers in Lesotho and similar countries grappling with data constraints and the need for timely economic analysis.
Keywords:
E. inflation forecasting; GDP Nowcasting result; D. GDP evaluation; C. GDP Nowcasting; ensemble Nowcasting model; Machine learning; Inflation; Africa; Global
Cover IMF Working Papers
Enhancing IMF Economics Training: AI-Powered Analysis of Qualitative Learner Feedback
Andras Komaromi
,
Xiaomin Wu
,
Ran Pan
,
Yang Liu
,
Pablo Cisneros
,
Anchal Manocha
, and
Hiba El Oirghi
The International Monetary Fund (IMF) has expanded its online learning program, offering over 100 Massive Open Online Courses (MOOCs) to support economic and financial policymaking worldwide. This paper explores the application of Artificial Intelligence (AI), specifically Large Language Models (LLMs), to analyze qualitative feedback from participants in these courses. By fine-tuning a pre-trained LLM on expert-annotated text data, we develop models that efficiently classify open-ended survey responses with accuracy comparable to human coders. The models’ robust performance across multiple languages, including English, French, and Spanish, demonstrates its versatility. Key insights from the analysis include a preference for shorter, modular content, with variations across genders, and the significant impact of language barriers on learning outcomes. These and other findings from unstructured learner feedback inform the continuous improvement of the IMF's online courses, aligning with its capacity development goals to enhance economic and financial expertise globally.
Keywords:
IMF Economics training; measuring training impact; learner feedback; application of artificial intelligence; learner satisfaction; Artificial intelligence; Machine learning; Information systems and platforms; Automation; Sub-Saharan Africa; Western Hemisphere; Middle East and Central Asia; Europe; Asia and Pacific
Cover Technical Notes and Manuals
Налоговое администрирование: основные возможности аналитики для управления комплаенс-рисками
Joshua Aslett
,
Gustavo González
,
Stuart Hamilton
, and
Miguel Pecho
В этой технической справке представлена аналитика для управления комплаенс-рисками в рамках налогового администрирования. Вместе с сопровождающим ее набором инструментов эта справка предназначена для использования в качестве стартового набора для содействия развитию потенциала в области комплаенс-планирования , оценки рисков и работы групп по сбору аналитических данных. В этой справке, разработанной в первую очередь для начинающих аналитиков, которые только приступают к работе в области налогового администрирования, представлены как теоретические, так и практические аспекты аналитики. Его инструментарий состоит из первоначального набора аналитических форм, призванных помочь реализовать представленную здесь теорию на практике в таких областях, как: 1) комплаенс-планирование; 2) составление профиля налогоплательщика; и 3) отбор кандидатов для проведения проверок.
Keywords:
налоговое администрирование; управление комплаенс-рисками; комплаенс-стратегия; анализ рисков; аналитические данные; данные; аналитика; цифровизация; информационные технологии
Cover IMF Working Papers
Harnessing Satellite Data to Improve Social Assistance Targeting in the Eastern Caribbean
Sophia Chen
,
Ryu Matsuura
,
Flavien Moreau
, and
Joana Pereira
Prioritizing populations most in need of social assistance is an important policy decision. In the Eastern Caribbean, social assistance targeting is constrained by limited data and the need for rapid support in times of large economic and natural disaster shocks. We leverage recent advances in machine learning and satellite imagery processing to propose an implementable strategy in the face of these constraints. We show that local well-being can be predicted with high accuracy in the Eastern Caribbean region using satellite data and that such predictions can be used to improve targeting by reducing aggregation bias, better allocating resources across areas, and proxying for information difficult to verify.
Keywords:
Social assistance targeting; satellite data; machine learning; Eastern Caribbean; Small Island Developing States.; aggregation bias; hard-to-verify information; satellite imagery processing; Income; Natural disasters; Caribbean
Cover IMF Working Papers
The Economic Impacts and the Regulation of AI: A Review of the Academic Literature and Policy Actions
Mariarosaria Comunale
and
Andrea Manera
We review the literature on the effects of Artificial Intelligence (AI) adoption and the ongoing regulatory efforts concerning this technology. Economic research encompasses growth, employment, productivity, and income inequality effects, while regulation covers market competition, data privacy, copyright, national security, ethics concerns, and financial stability. We find that: (i) theoretical research agrees that AI will affect most occupations and transform growth, but empirical findings are inconclusive on employment and productivity effects; (ii) regulation has focused primarily on topics not explored by the academic literature; (iii) across countries, regulations differ widely in scope and approaches and face difficult trade-offs.
Keywords:
Artificial Intelligence (AI); labor market; task exposure; productivity; regulation; governance.; productivity effect; regulation of AI; AI system; AI model; AI technology; Artificial intelligence; Machine learning; Automation; Global
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 Technical Notes and Manuals
Tax Administration: Essential Analytics for Compliance Risk Management
Joshua Aslett
,
Gustavo González
,
Stuart Hamilton
, and
Miguel Pecho
This technical note introduces analytics for compliance risk management in tax administration. Together with its accompanying toolkit, the note is intended as a starter kit to support capacity development in compliance planning, risk, and intelligence groups. Developed primarily for emerging analysts new to tax administration, the note presents both theory and practical aspects of analytics. Its toolkit is comprised of an initial collection of analytics templates designed to assist in turning the theory presented into practice in the areas of: (1) compliance planning; (2) taxpayer profiling; and (3) audit case selection.
Keywords:
tax administration; compliance risk management; compliance strategy; risk analysis; intelligence; data; analytics; digitalization; information technology; analytics support compliance risk management; support CRM analytics capability; CRM theory; IMF Library; data quality; Tax administration core functions; Machine learning; Value-added tax
Cover IMF Working Papers
Political Fragility: Coups d’État and Their Drivers
Aliona Cebotari
,
Enrique Chueca-Montuenga
,
Yoro Diallo
,
Yunsheng Ma
,
Rima A Turk
,
Weining Xin
, and
Harold Zavarce
The paper explores the drivers of political fragility by focusing on coups d’état as symptomatic of such fragility. It uses event studies to identify factors that exhibit significantly different dynamics in the runup to coups, and machine learning to identify these stressors and more structural determinants of fragility—as well as their nonlinear interactions—that create an environment propitious to coups. The paper finds that the destabilization of a country’s economic, political or security environment—such as low growth, high inflation, weak external positions, political instability and conflict—set the stage for a higher likelihood of coups, with overlapping stressors amplifying each other. These stressors are more likely to lead to breakdowns in political systems when demographic pressures and underlying structural weaknesses (especially poverty, exclusion, and weak governance) are present or when policies are weaker, through complex interactions. Conversely, strengthened fundamentals and macropolicies have higher returns in structurally fragile environments in terms of staving off political breakdowns, suggesting that continued engagement by multilateral institutions and donors in fragile situations is likely to yield particularly high dividends. The model performs well in predicting coups out of sample, having predicted a high probability of most 2020-23 coups, including in the Sahel region.
Keywords:
Fragility; Drivers of Fragility; Coup d’État; Machine Learning; coup dynamics; event study result; coup probability; probability of coup; IMF working paper 24/34; Fiscal stance; Income; Sub-Saharan Africa; Middle East and Central Asia; Caribbean
Cover IMF Working Papers
Reported Social Unrest Index: August 2023 Update
Mr. Philip Barrett
and
Euihyun Bae
This paper is the second update of the Reported Social Unrest Index (Barrett et al. 2022), outlining developments in global social unrest since March 2022. It shows that the fraction of countries experiencing major social unrest events has been stable. Reasons for social unrest can be broadly categorized as stemming from sdebate over constitutional issues, protests connected to specific policies, and other generalized disorder.
Keywords:
attempts to proxy; fraction of country; copyright page; update of the Reported Social Unrest Index; IMF surveillance; Pension spending; Machine learning; Pension reform; Stock markets; COVID-19; Global