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Cover IMF Working Papers
Nowcasting GDP - A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies
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.
Keywords:
Nowcasting, Factor Model, Machine Learning, Large Data Sets; machine learning algorithm; novel data; approach Using DFM; support vector Machine; data availability; Machine learning; COVID-19; Business cycles; Factor models; Global; Caribbean; Europe
Cover IMF Working Papers
Seeing in the Dark: A Machine-Learning Approach to Nowcasting in Lebanon
Mr. Andrew J Tiffin
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.
Keywords:
WP; GDP; Macroeconomic Forecasts; Nowcasting; Random Forests; Elastic Net; LASSO; Statistical Learning; Cross Validation; Ensemble; Variable Selection; Lebanon; GDP data; coefficient estimate; ridge regression; regression tree; GDP growth; machine-learning technique; GDP movement; GDP release; Machine learning; Cyclical indicators