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.
Jin-Kyu Jung, Manasa Patnam, and Anna Ter-Martirosyan
Forecasting macroeconomic variables is key to developing a view on a country's economic outlook.
Most traditional forecasting models rely on fitting data to a pre-specified relationship between input
and output variables, thereby assuming a specific functional and stochastic process underlying that
process. We pursue a new approach to forecasting by employing a number of machine learning
algorithms, a method that is data driven, and imposing limited restrictions on the nature of the true
relationship between input and output variables. We apply the Elastic Net, SuperLearner, and
Recurring Neural Network algorithms on macro data of seven, broadly representative, advanced and
emerging economies and find that these algorithms can outperform traditional statistical models,
thereby offering a relevant addition to the field of economic forecasting.
International Monetary Fund. Strategy, Policy, & and Review Department
"The first data and statistics strategy for the Fund comes at a critical time. A fast-changing data landscape, new data needs for evolving surveillance priorities, and persisting data weaknesses across the membership pose challenges and opportunities for the Fund and its members. The challenges emerging from the digital revolution include an unprecedented amount of new data and measurement questions on growth, productivity, inflation, and welfare. Newly available granular and high-frequency (big) data offer the potential for more timely detection of vulnerabilities. In the wake of the crisis, Fund surveillance requires greater cross-country data comparability; staff and authorities face the complexity of integrating new data sources and closing data gaps, while working to address the weaknesses noted by the IEO Report (Behind the Scenes with Data at the IMF) in 2016.
The overarching strategy is to move toward an ecosystem of data and statistics that enables the Fund and its members to better meet the evolving data needs in a digital world. It integrates Fund-wide work streams on data provision to the Fund for surveillance purposes, international statistical standards, capacity development, and data management under a common institutional objective. It seeks seamless access and sharing of data within the Fund, enabling cloud-based data dissemination to support data provision by member countries (e.g., the “global data commons”), closing data gaps with new sources including Big Data, and improving assessments of data adequacy for surveillance to help better prioritize capacity development. The Fund also will work with policymakers to understand the implications of the digital economy and digital data for the macroeconomic statistics, including new measures of welfare beyond GDP."
International Monetary Fund. Communications Department
Address at the Bank of England Twentieth Anniversary Conference
September 29, 2017
International Monetary Fund Managing Director Christine Lagarde delivered this address at the Bank of England conference, “Independence—20 Years On” in London, U.K., on September 29, 2017.
Macroeconomic analysis in Lebanon presents a distinct challenge. For example, long
delays in the publication of GDP data mean that our analysis often relies on proxy
variables, and resembles an extended version of the “nowcasting” challenge familiar to
many central banks. Addressing this problem—and mindful of the pitfalls of extracting
information from a large number of correlated proxies—we explore some recent
techniques from the machine learning literature. We focus on two popular techniques
(Elastic Net regression and Random Forests) and provide an estimation procedure that is
intuitively familiar and well suited to the challenging features of Lebanon’s data.