{
  "title": "Short-Term Forecasting: Projecting Italian GDPone Quarter to Two Years Ahead",
  "publication": "IMF Working Papers, August 1, 2001",
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  "summary": "This paper presents a \"bridge model\" for short-run (one or two quarters ahead) forecasting of Italian GDP, relying on industrial production and survey indicators as key variables that can help in providing a real-time first GDP estimate.",
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    {
      "heading": "Summary",
      "content": "- Paper presents a \"bridge model\" for short-run (one or two quarters ahead) forecasting of Italian GDP, relying on industrial production and survey indicators as key variables that can help in providing a real-time first GDP estimate.\n- For a one- to two-year horizon, the paper formulates and estimates a Bayesian VAR (BVAR) model of the Italian economy.\n- Both the \"bridge\" and the BVAR model can supplement traditional judgmental or structural econometric forecasts.\n- Given their simplicity and their good forecasting power, the framework may be usefully extended to other variables as well as to other countries."
    },
    {
      "heading": "Models and methods",
      "content": "- Bridge model:\n  - Designed for short-run (one or two quarters ahead) GDP forecasting.\n  - Relies on industrial production and survey indicators as key predictors for real-time first GDP estimates.\n- Bayesian VAR (BVAR) model:\n  - Formulated and estimated for a one- to two-year forecasting horizon of the Italian economy.\n- Intended use:\n  - Supplementing traditional judgmental or structural econometric forecasts.\n  - Potential extension to other variables and other countries."
    },
    {
      "heading": "Key findings and claims",
      "content": "- The bridge model and the BVAR model exhibit \"good forecasting power\" for their respective horizons.\n- Simplicity of the framework is emphasized as an advantage for practical forecasting applications."
    },
    {
      "heading": "Subjects and keywords",
      "content": "- Subject: Cyclical indicators, Econometric analysis, Economic growth, GDP forecasting, Industrial production, National accounts, Production, Production index, Vector autoregression\n- Keywords: Bayesian Vector Autoregressions, Cyclical indicators, Forecasting, GDP, GDP estimate, GDP forecast, GDP forecasting, GDP growth, growth forecast, growth YOY, Industrial production, leading indicators, Production index, Vector autoregression, WP\n\n---\n\n Content in this bundle\n\n- Short-Term Forecasting:  Projecting Italian GDP, One Quarter to Two Years Ahead - WP/01/109\n  - Short-Term Forecasting:  Projecting Italian GDP, One Quarter to Two Years Ahead - WP/01/109 (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Short-Term Forecasting:  Projecting Italian GDP, One Quarter to Two Years Ahead - WP/01/109 (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2016/12/30/short-term-forecasting-projecting-italian-gdpone-quarter-to-two-years-ahead-15275"
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    "Authors: Matteo Iacoviello",
    "Published: August 1, 2001",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9781451853216.001",
    "Paper presents a \"bridge model\" for short-run (one or two quarters ahead) forecasting of Italian GDP, relying on industrial production and survey indicators as key variables that can help in providing a real-time first GDP estimate.",
    "For a one- to two-year horizon, the paper formulates and estimates a Bayesian VAR (BVAR) model of the Italian economy.",
    "Both the \"bridge\" and the BVAR model can supplement traditional judgmental or structural econometric forecasts.",
    "Given their simplicity and their good forecasting power, the framework may be usefully extended to other variables as well as to other countries.",
    "Bridge model:",
    "Bayesian VAR (BVAR) model:",
    "Intended use:",
    "The bridge model and the BVAR model exhibit \"good forecasting power\" for their respective horizons.",
    "Simplicity of the framework is emphasized as an advantage for practical forecasting applications.",
    "Subject: Cyclical indicators, Econometric analysis, Economic growth, GDP forecasting, Industrial production, National accounts, Production, Production index, Vector autoregression",
    "Keywords: Bayesian Vector Autoregressions, Cyclical indicators, Forecasting, GDP, GDP estimate, GDP forecast, GDP forecasting, GDP growth, growth forecast, growth YOY, Industrial production, leading indicators, Production index, Vector autoregression, WP",
    "**Short-Term Forecasting:  Projecting Italian GDP, One Quarter to Two Years Ahead - WP/01/109**"
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