A Newton's Method for Benchmarking Time Series According to a Growth Rates Preservation Principle [electronic resource] / Marco Marini.

By: Marini, MarcoContributor(s): Di Fonzo, Tommaso | Marini, MarcoMaterial type: TextTextSeries: IMF Working Papers; Working Paper ; No. 11/179Publication details: Washington, D.C. : International Monetary Fund, 2011Description: 1 online resource (42 p.)ISBN: 1462311296 :ISSN: 1018-5941Subject(s): Benchmarking | Computation | Estimating | Linearly Equality Constrained Non-Linear Optimization | Methodology for Collecting | Minimization | ThailandAdditional physical formats: Print Version:: A Newton's Method for Benchmarking Time Series According to a Growth Rates Preservation PrincipleOnline resources: IMF e-Library | IMF Book Store Abstract: 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). A procedure is developed which (i) transforms the original constrained problem into an unconstrained one, and (ii) applies a Newton's method exploiting the analytic Hessian of the GRP objective function. We show that the proposed technique is easy to implement, computationally robust and efficient, all features which make it a plausible competitor of other benchmarking procedures (Denton, 1971; Dagum and Cholette, 2006) also in a data-production process involving a considerable amount of series.
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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). A procedure is developed which (i) transforms the original constrained problem into an unconstrained one, and (ii) applies a Newton's method exploiting the analytic Hessian of the GRP objective function. We show that the proposed technique is easy to implement, computationally robust and efficient, all features which make it a plausible competitor of other benchmarking procedures (Denton, 1971; Dagum and Cholette, 2006) also in a data-production process involving a considerable amount of series.

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