{
  "title": "Seeing in the Dark: A Machine-Learning Approach to Nowcasting in Lebanon",
  "publication": "IMF Working Papers, March 8, 2016",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2016/12/31/seeing-in-the-dark-a-machine-learning-approach-to-nowcasting-in-lebanon-43779",
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  "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.",
  "sections": [
    {
      "heading": "Summary",
      "content": "- Macroeconomic analysis in Lebanon faces long delays in the publication of GDP data, forcing reliance on proxy variables and creating an extended “nowcasting” challenge.\n- 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.\n- Focus is placed on two popular techniques: Elastic Net regression and Random Forests.\n- The paper provides an estimation procedure described as intuitively familiar and well suited to the challenging features of Lebanon’s data."
    },
    {
      "heading": "Methods and Technical Focus",
      "content": "- Techniques examined:\n  - Elastic Net regression\n  - Random Forests\n- Related statistical and machine-learning concepts emphasized:\n  - LASSO\n  - ridge regression\n  - regression tree\n  - Ensemble methods\n  - Cross Validation\n  - Variable Selection\n  - Statistical Learning"
    },
    {
      "heading": "Subject Areas and Keywords",
      "content": "- Subject: Cyclical indicators, Economic forecasting, Economic growth, Machine learning, Technology\n- 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\n\n---\n\n Content in this bundle\n\n- wp1656\n  - wp1656 (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - wp1656 (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2016/12/31/seeing-in-the-dark-a-machine-learning-approach-to-nowcasting-in-lebanon-43779"
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    "Authors: Andrew J Tiffin",
    "Published: March 8, 2016",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9781513568089.001",
    "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.",
    "Techniques examined:",
    "Related statistical and machine-learning concepts emphasized:",
    "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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