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.

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.

Inequalities are increasing across the world and living conditions are very unequal between different parts of the world. Some people can live healthy, rich, and happy lives while others continue to live in poor health, poverty, and grief. Inequalities have greatly strengthened the economic and political power of those people at the top. This volume is titled “Global Inequalities and Polarization” and contains eight selected articles that approach inequality and polarization from different angles.

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.

Businesses must create initiatives and adopt eco-friendly practices in order to adhere to the sustainability goals of a globalized world. Recycling, product service systems, and green manufacturing are just a few methods businesses use within a sustainable supply chain. However, these tools and techniques must also ensure business growth in order to remain relevant in an environmentally-conscious world. The Handbook of Research on Interdisciplinary Approaches to Decision Making for Sustainable Supply Chains provides interdisciplinary approaches to sustainable supply chain management through the optimization of system performance and development of new policies, design networks, and effective reverse logistics practices. Featuring research on topics such as industrial symbiosis, green collaboration, and clean transportation, this book is ideally designed for policymakers, business executives, warehouse managers, operations managers, suppliers, industry professionals, sustainability developers, decision makers, students, academicians, practitioners, and researchers seeking current research on reducing the environmental impacts of businesses via sustainable supply chain planning.

In this study we introduce and apply a set of machine learning and artificial intelligence techniques to analyze multi-dimensional fragility-related data. Our analysis of the fragility data collected by the OECD for its States of Fragility index showed that the use of such techniques could provide further insights into the non-linear relationships and diverse drivers of state fragility, highlighting the importance of a nuanced and context-specific approach to understanding and addressing this multi-aspect issue. We also applied the methodology used in this paper to South Sudan, one of the most fragile countries in the world to analyze the dynamics behind the different aspects of fragility over time. The results could be used to improve the Fund’s country engagement strategy (CES) and efforts at the country.

This book explores various aspects of data engineering and information processing. In this second volume, the authors assess the challenges and opportunities involved in doing business with information. Their contributions on business information processing and management reflect diverse viewpoints – not only technological, but also business and social. As the global marketplace grows more and more complex due to the increasing availability of data, the information business is steadily gaining popularity and has a huge impact on modern society. Thus, there is a growing need for consensus on how business information can be created, accessed, used and managed.

Ιn this dissertation I forecast financial time series with machine learning methodologies.During my research I propose various novel forecasting schemes and attack four problems in a machine learning approach: short and long-term exchange rate, housing prices and bank insolvencies forecasting. More specifically, I propose a novel forecasting methodology in short-term exchange rate forecasting that couples a machine learning with a signal processing technique. In the same field I consider machine learning in long-term forecasting, that has rarely been used before in the relevant literature. The machine learning models outperform all the econometric models examined in this dissertation in terms of forecasting error and directional forecasting accuracy Overall, the empirical findings reveal the superiority of machine learning to econometric models in forecasting the selected financial time series examined in this dissertation.

Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.

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.