{
  "title": "Predictive Density Aggregation: A Model for Global GDP Growth",
  "publication": "IMF Working Papers, May 29, 2020",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2020/05/29/predictive-density-aggregation-a-model-for-global-gdp-growth-49441",
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  "summary": "In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries’ predictive GDP growth densities, taking into account cross-country interdependencies. Speci?",
  "sections": [
    {
      "heading": "Methodological approach",
      "content": "- Proposes a bottom-up probabilistic model to obtain the predictive density of global GDP growth.\n- Estimates and combines single countries’ predictive GDP growth densities while taking into account cross-country interdependencies.\n- Each economy’s predictive density depends on a set of observable country-specific factors.\n- Uses sampling techniques to aggregate individual countries’ densities into a world aggregate while preserving the non-i.i.d. nature of the global GDP growth distribution."
    },
    {
      "heading": "Modeling contemporaneous interdependencies",
      "content": "- Models non-parametrically the contemporaneous interdependencies across the United States, the euro area, and China via a conditional kernel density estimation of a joint distribution.\n- Characterizes potential amplification effects stemming from other large economies in each region using kernel density estimations.\n- Models the reaction of all other economies with parametric assumptions."
    },
    {
      "heading": "Estimation and aggregation",
      "content": "- Combines non-parametric kernel density estimations for key large economies with parametric assumptions for other economies.\n- Aggregation is performed via sampling techniques to preserve cross-country dependence and the non-i.i.d. global distribution."
    },
    {
      "heading": "Evaluation and performance",
      "content": "- Out-of-sample metrics confirm the accuracy of the approach.\n\n---\n\n Content in this bundle\n\n- Working Paper\n  - Working Paper (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Working Paper (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2020/05/29/predictive-density-aggregation-a-model-for-global-gdp-growth-49441"
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    "Authors: Francesca Caselli, Francesco Grigoli, Romain Lafarguette, Changchun Wang",
    "Published: May 29, 2020",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9781513545653.001",
    "Proposes a bottom-up probabilistic model to obtain the predictive density of global GDP growth.",
    "Estimates and combines single countries’ predictive GDP growth densities while taking into account cross-country interdependencies.",
    "Each economy’s predictive density depends on a set of observable country-specific factors.",
    "Uses sampling techniques to aggregate individual countries’ densities into a world aggregate while preserving the non-i.i.d. nature of the global GDP growth distribution.",
    "Models non-parametrically the contemporaneous interdependencies across the United States, the euro area, and China via a conditional kernel density estimation of a joint distribution.",
    "Characterizes potential amplification effects stemming from other large economies in each region using kernel density estimations.",
    "Models the reaction of all other economies with parametric assumptions.",
    "Combines non-parametric kernel density estimations for key large economies with parametric assumptions for other economies.",
    "Aggregation is performed via sampling techniques to preserve cross-country dependence and the non-i.i.d. global distribution.",
    "Out-of-sample metrics confirm the accuracy of the approach.",
    "**Working Paper**"
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