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

Author/Editor:

Andrew J Tiffin

Publication Date:

March 8, 2016

Electronic Access:

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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.

Series:

Working Paper No. 16/56

Subject:

English

Publication Date:

March 8, 2016

ISBN/ISSN:

9781513568089/1018-5941

Stock No:

WPIEA2016056

Price:

$18.00 (Academic Rate:$18.00)

Format:

Paper

Pages:

20

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