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

21 Pages Posted: 26 Apr 2016

See all articles by Andrew Tiffin

Andrew Tiffin

International Monetary Fund (IMF)

Date Written: March 2016


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

Suggested Citation

Tiffin, Andrew, Seeing in the Dark: A Machine-Learning Approach to Nowcasting in Lebanon (March 2016). IMF Working Paper No. 16/56, Available at SSRN:

Andrew Tiffin (Contact Author)

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

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