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

_IMF Working Papers, March 8, 2016_

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## Bibliographic details
- Authors: Andrew J Tiffin
- Published: March 8, 2016
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9781513568089.001

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### Summary
- Macroeconomic analysis in Lebanon faces long delays in the publication of GDP data, forcing reliance on proxy variables and creating an extended “nowcasting” challenge.
- The paper explores recent techniques from the machine learning literature to address this problem, while being mindful of pitfalls when extracting information from a large number of correlated proxies.
- Focus is placed on two popular techniques: Elastic Net regression and Random Forests.
- The paper provides an estimation procedure described as intuitively familiar and well suited to the challenging features of Lebanon’s data.

### Methods and Technical Focus
- Techniques examined:
  - Elastic Net regression
  - Random Forests
- Related statistical and machine-learning concepts emphasized:
  - LASSO
  - ridge regression
  - regression tree
  - Ensemble methods
  - Cross Validation
  - Variable Selection
  - Statistical Learning

### Subject Areas and Keywords
- 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

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_Source: https://www.imf.org/en/publications/wp/issues/2016/12/31/seeing-in-the-dark-a-machine-learning-approach-to-nowcasting-in-lebanon-43779_