A Newton's Method for Benchmarking Time Series According to a Growth Rates Preservation Principle
July 1, 2011
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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). 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.
Subject: 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
Pages:
42
Volume:
2011
DOI:
Issue:
179
Series:
Working Paper No. 2011/179
Stock No:
WPIEA2011179
ISBN:
9781462311293
ISSN:
1018-5941
