01914cas a2200313 a 4500 AALejournalIMF016687 230101c9999 xx r poo 0 0eng d 5.00 USD 9781513568089 1018-5941 BD-DhAAL BD-DhAAL Tiffin, Andrew. Seeing in the Dark : A Machine-Learning Approach to Nowcasting in Lebanon / Andrew Tiffin. Washington, D.C. : International Monetary Fund, 2016. 1 online resource (20 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 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. Mode of access: Internet GDP imf Macroeconomic Forecasts imf Nowcasting imf Random Forests imf WP imf Lebanon imf IMF Working Papers; Working Paper ; No. 2016/056 Full text available on IMF http://elibrary.imf.org/view/journals/001/2016/056/001.2016.issue-056-en.xml IMF e-Library