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

March 8, 2016

Preview Citation

Format: Chicago

Andrew J Tiffin. Seeing in the Dark: A Machine-Learning Approach to Nowcasting in Lebanon, (USA: International Monetary Fund, 2016) accessed November 21, 2024

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

Summary

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.

Subject: Cyclical indicators, Economic forecasting, Economic growth, Machine learning, Technology

Keywords: Coefficient estimate, Cross Validation, Cyclical indicators, Elastic Net, Ensemble, GDP, GDP data, GDP growth, GDP movement, GDP release, LASSO, Lebanon, Machine learning, Machine-learning technique, Macroeconomic Forecasts, Nowcasting, Random Forests, Regression tree, Ridge regression, Statistical Learning, Variable Selection, WP

Publication Details

Pages:

20
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2016/056
Stock No:

WPIEA2016056
ISBN:

9781513568089
ISSN:

1018-5941

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