{
  "title": "A Newton's Method for Benchmarking Time Series According to a Growth Rates Preservation Principle",
  "publication": "IMF Working Papers, July 1, 2011",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2016/12/31/a-newton-s-method-for-benchmarking-time-series-according-to-a-growth-rates-preservation-25101",
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  "summary": "This work presents a new technique for temporally benchmarking a time series according to the growth rates preservation principle (GRP) by Causey and Trager (1981).",
  "sections": [
    {
      "heading": "Summary",
      "content": "- Presents a new technique for temporally benchmarking a time series according to the growth rates preservation principle (GRP) by Causey and Trager (1981).\n- Develops a procedure which:\n  - (i) transforms the original constrained problem into an unconstrained one, and\n  - (ii) applies a Newton's method exploiting the analytic Hessian of the GRP objective function.\n- Claims the proposed technique is easy to implement, computationally robust and efficient, making it a plausible competitor of other benchmarking procedures (Denton, 1971; Dagum and Cholette, 2006) in data-production processes involving a considerable amount of series."
    },
    {
      "heading": "Methodology and Technical Features",
      "content": "- Core principle: Growth Rates Preservation (GRP) criterion.\n- Mathematical/algorithmic elements emphasized:\n  - Newton’s method applied to the GRP objective function.\n  - Use of the analytic Hessian matrix within the Newton framework.\n  - Transformation from a linearly equality constrained non-linear optimization problem to an unconstrained minimization problem.\n- Comparisons and benchmarks:\n  - Positioned as competitor to Denton (1971) and Dagum and Cholette (2006) benchmarking procedures.\n  - Mentions BFGS performance in keyword set (implying consideration of alternative optimization algorithms)."
    },
    {
      "heading": "Findings and Advantages",
      "content": "- The technique is:\n  - Easy to implement.\n  - Computationally robust.\n  - Efficient for processing a considerable amount of series.\n- The approach preserves movement/growth rates according to the GRP objective."
    },
    {
      "heading": "Applications and Relevance",
      "content": "- Targeted at temporal benchmarking of time series where preservation of growth rates is desired.\n- Suitable for data-production processes involving many series.\n- Subject classifications: Artificial intelligence, Technology.\n- Keywords: Artificial intelligence, benchmarked estimate, Benchmarking, benchmarking procedure, BFGS performance, Global, GRP criterion, GRP procedure, Hessian matrix, Linearly equality constrained non-linear optimization, minimization problem, Movement preservation, Newton’s method, objective function, optimization procedure, time series, WP\n\n---\n\n Content in this bundle\n\n- wp11179\n  - wp11179 (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - wp11179 (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2016/12/31/a-newton-s-method-for-benchmarking-time-series-according-to-a-growth-rates-preservation-25101"
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    "Authors: Marco Marini, Tommaso Di Fonzo",
    "Published: July 1, 2011",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9781462311293.001",
    "Presents a new technique for temporally benchmarking a time series according to the growth rates preservation principle (GRP) by Causey and Trager (1981).",
    "Develops a procedure which:",
    "Claims the proposed technique is easy to implement, computationally robust and efficient, making it a plausible competitor of other benchmarking procedures (Denton, 1971; Dagum and Cholette, 2006) in data-production processes involving a considerable amount of series.",
    "Core principle: Growth Rates Preservation (GRP) criterion.",
    "Mathematical/algorithmic elements emphasized:",
    "Comparisons and benchmarks:",
    "The technique is:",
    "The approach preserves movement/growth rates according to the GRP objective.",
    "Targeted at temporal benchmarking of time series where preservation of growth rates is desired.",
    "Suitable for data-production processes involving many series.",
    "Subject classifications: Artificial intelligence, Technology.",
    "Keywords: Artificial intelligence, benchmarked estimate, Benchmarking, benchmarking procedure, BFGS performance, Global, GRP criterion, GRP procedure, Hessian matrix, Linearly equality constrained non-linear optimization, minimization problem, Movement preservation, Newton’s method, objective function, optimization procedure, time series, WP",
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