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

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.

Keywords: Macroeconomic Forecasts, Nowcasting, Random Forests, Elastic Net, LASSO, Statistical Learning, Cross Validation, Ensemble, Variable Selection, gdp, data, variables, prediction, value, General

JEL Classification: C80, C15, C44, C52, C53, C63, E30