European Parliament Library

Seeing in the Dark :, A Machine-Learning Approach to Nowcasting in Lebanon, Andrew Tiffin

Abstract
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
Language
eng
Literary Form
non fiction
Physical Description
1 online resource (20 p.)
Form Of Item
online
Isbn
9781513569642

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