02266cas a2200337 a 4500 AALejournalIMF022505 230101c9999 xx r poo 0 0eng d 5.00 USD 9798400204425 1018-5941 BD-DhAAL BD-DhAAL Dauphin, Jean-Francois. Nowcasting GDP : A Scalable Approach Using DFM, Machine Learning and Novel Data, Applied to European Economies / Jean-Francois Dauphin, Kamil Dybczak, Morgan Maneely, Marzie Taheri Sanjani. Washington, D.C. : International Monetary Fund, 2022. 1 online resource (45 pages) IMF Working Papers <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required Electronic access restricted to authorized BRAC University faculty, staff and students 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. Mode of access: Internet Forecasting and Other Model Applications imf Machine Learning Algorithm imf Monetary Policy imf Multiple or Simultaneous Equation Models imf Nowcasting, Factor Model, Machine Learning and Large Data Sets imf Dybczak, Kamil. Maneely, Morgan. Taheri Sanjani, Marzie. IMF Working Papers; Working Paper ; No. 2022/052 Full text available on IMF http://elibrary.imf.org/view/journals/001/2022/052/001.2022.issue-052-en.xml IMF e-Library