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.
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.
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.
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.
The recent relatively high levels of global oil prices have led to a significant improvement in the public finances of several hydrocarbon-exporting countries. However, despite the increase in fiscal buffers, medium-term risks remain high. Fiscal vulnerabilities have increased as a consequence of the substantial spending packages that have been implemented in recent years. This has raised break-even prices—that is, the price levels that ensure that fiscal accounts are in balance at a given level of spending—in these countries. This study analyses such risks and develops measures of fiscal risk stemming from oil price fluctuations. An empirical application to hydrocarbon-exporting countries from the Middle East and North Africa region is included. Additionally, it is noted that countries with large net assets and proven oil reserves are much less vulnerable to fiscal risk than is indicated by standard measures based on break-even prices.