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Mr. Jean-Francois Dauphin, Mr. Kamil Dybczak, Morgan Maneely, Marzie Taheri Sanjani, Mrs. Nujin Suphaphiphat, Yifei Wang, and Hanqi Zhang
This paper describes recent work to strengthen nowcasting capacity at the IMF’s European department. It motivates and compiles datasets of standard and nontraditional variables, such as Google search and air quality. It applies standard dynamic factor models (DFMs) and several machine learning (ML) algorithms to nowcast GDP growth across a heterogenous group of European economies during normal and crisis times. Most of our methods significantly outperform the AR(1) benchmark model. Our DFMs tend to perform better during normal times while many of the ML methods we used performed strongly at identifying turning points. Our approach is easily applicable to other countries, subject to data availability.
Majid Bazarbash

Gradient Boosting Trees Support Vector Machines (SVMs) Neural Networks C. How Does Machine Learning Differ from Econometrics? IV. Strengths and Weaknesses of ML-Based Lending for Financial Inclusion A. Strengths of ML-Based Lending ML can make assessing credit risk of small borrowers feasible and economical ML can harden soft information ML can better capture nonlinearities ML could mitigate information asymmetry B. Weaknesses of ML-Based Lending ML-based lending bears risks of financial exclusion ML-based credit rating could cause

Mr. Jean-Francois Dauphin, Mr. Kamil Dybczak, Morgan Maneely, Marzie Taheri Sanjani, Mrs. Nujin Suphaphiphat, Yifei Wang, and Hanqi Zhang

Integrated Tool 4. Machine Learning Algorithms 1. Regularized Regression methods 2. Support Vector Machine 3. Random Forest 4. Neural Network 5. Indicators of predictive accuracy: Models by Country References FIGURES Figure 1. Examples of Model Performances TABLES Table 1. A Brief Introduction to ML Algorithms Table 2. Full Sample- Model Performance (RMSE) Until 2021Q1 Table 3. Pre-COVID Samples- Model Performance (RMSE) Until 2019Q4 Table 4. During COVID-19 Sample- Model Performance (RMSE) Between 2020Q1-2021Q1 Glossary

Chris Redl and Sandile Hlatshwayo

: Machine learning models 7.1 Linear models: regularized logistic regression 7.2 Neural Network 7.3 Support vector machine 7.4 Tree based models 7.4.1 AdaBoost 7.4.2 Gradient Boosted Trees 8 Appendix II: Input Data and Aggregation scheme

Ms. Burcu Hacibedel and Ritong Qu

Corporate Distress: Initial Findings 6 Conclusion Tables and Figures Appendix A MCMC Algorithm to Identify Corporate Distress Appendix B Constructing Predictors from Compustat Global Appendix C Machine Learning Models and Hyperparameter Selection C.1 Logistic Regression with Regularization C.2 Random Forest C.3 Support Vector Machine C.4 Linear Discriminant Analysis C.5 Extreme Gradient Boosting Tree * The views expressed in this paper are those of the author(s) and do not necessarily represent the views of the IMF, its Executive

Majid Bazarbash
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.
Majid Bazarbash

forest, gradient boosting trees, support vector machines, and neural networks. The paper presents the fundamental ideas underpinning these methods, and discusses their strengths, weaknesses, and extensions. While the discussion avoids mathematical detail and computational methods involved in applying the algorithms, it provides sufficient background for a nontechnical learner to understand the models. The core philosophy of ML is to apply potentially complicated algorithms that could be run by machines to learn patterns in data with the primary goal of making

Mr. Jean-Francois Dauphin, Mr. Kamil Dybczak, Morgan Maneely, Marzie Taheri Sanjani, Mrs. Nujin Suphaphiphat, Yifei Wang, and Hanqi Zhang

—mainly boosted trees, support vector machine, and neural networks—are able to outperform a simple autoregressive model and DFM. Applying several machine learning algorithms on a dataset of quarterly macroeconomic and financial data to nowcast Indonesia’s GDP growth, Muchishaet. al. (2021) arrive at a similar conclusion that all machine learning models outperform AR(1) bench mark while Random Forest showed the best performance. Without adding data from more granular and novel data sources, Jung et. al. (2018) still find that machine learning methods—Elastic Net, Super Learner

Marijn A. Bolhuis and Brett Rayner
We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based models and use several machine learning models to predict real output growth with lower forecast errors than traditional models. By combining multiple machine learning models into ensembles, we lower forecast errors even further. We also identify measures of variable importance to help improve the transparency of machine learning-based forecasts. Applying the framework to Turkey reduces forecast errors by at least 30 percent relative to traditional models. The framework also better predicts economic volatility, suggesting that machine learning techniques could be an important part of the macro forecasting toolkit of many countries.