Description

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.

More publications in this series: Working Papers

More publications by: Andrew Tiffin