This paper presents a set of collaborative filtering algorithms that produce product recommendations to diversify and optimize a country's export structure in support of sustainable long-term growth. The recommendation system is able to accurately predict the historical trends in export content and structure for high-growth countries, such as China, India, Poland, and Chile, over 20-year spans. As a contemporary case study, the system is applied to Paraguay, to create recommendations for the country's export diversification strategy.
We leverage insights from machine learning to optimize the tradeoff between bias and
variance when estimating economic models using pooled datasets. Specifically, we develop a
simple algorithm that estimates the similarity of economic structures across countries and
selects the optimal pool of countries to maximize out-of-sample prediction accuracy of a
model. We apply the new alogrithm by nowcasting output growth with a panel of 102
countries and are able to significantly improve forecast accuracy relative to alternative pools.
The algortihm improves nowcast performance for advanced economies, as well as emerging
market and developing economies, suggesting that machine learning techniques using pooled
data could be an important macro tool for many countries.
Ms. Ghada Fayad, Chengyu Huang, Yoko Shibuya, and Peng Zhao
This paper applies state-of-the-art deep learning techniques to develop the first sentiment index measuring member countries’ reception of IMF policy advice at the time of Article IV Consultations. This paper finds that while authorities of member countries largely agree with Fund advice, there is variation across country size, external openness, policy sectors and their assessed riskiness, political systems, and commodity export intensity. The paper also looks at how sentiment changes during and after a financial arrangement or program with the Fund, as well as when a country receives IMF technical assistance. The results shed light on key aspects on Fund surveillance while redefining how the IMF can view its relevance, value added, and traction with its member countries.
We compared the predictive performance of a series of machine learning and traditional methods for monthly CDS spreads, using firms’ accounting-based, market-based and macroeconomics variables for a time period of 2006 to 2016. We find that ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and Bagging particularly stands out in terms of accuracy. Traditional credit risk models using OLS techniques have the lowest out-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add considerable value to existent credit risk prediction accuracy and enable CDS shadow pricing for companies missing those securities.
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.
International Monetary Fund. Western Hemisphere Dept.
This Selected Issues explores ways for strengthening the current fiscal framework in Suriname and considers options for a new fiscal anchor. The paper provides an overview of mineral natural resources and their importance for the budget. It also lays out the current framework for fiscal planning and budget execution in Suriname and discusses the analytical underpinnings of modernizing it to make it more robust. The paper also presents estimates of long-term sustainability benchmarks based on the IMF’s policy toolkit for resource-rich developing countries. Suriname’s fiscal framework can be strengthened through a fiscal anchor rooted in the non-resource primary balance. Given the size of fiscal adjustment required to bring the non-resource primary balance in line with the long-term sustainability benchmark, a substantial transition period is needed to implement it. The IMF Staff’s adjustment scenario—designed to put public debt on the downward path—closes the current gap by less than half, implying that adjustment would need to continue beyond the 5-year horizon.