Abstract
We present a Newton’s method with Hessian modification for benchmarking a time series according to a growth rates preservation principle. Unlike the well-known proportionate first differences solution by [7], this technique is based on a more natural measure of the movement of the preliminary series, whose dynamic profile is aimed to be preserved as much as possible by the benchmarked series. The computational issues arising from the nonlinearity of the problem can be dealt with by a computationally robust and efficient approach, which results in an effective statistical tool also in a data-production process involving a considerable amount of series.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bloem, A., Dippelsman, R., Mæhle, N.: Quarterly national accounts manual. Concepts, Data Sources, and Compilation. International Monetary Fund, Washington DC (2001)
Bozik, J.E., Otto, M.C.: Benchmarking: evaluating methods that preserve month-to-month changes. Bureau of the Census – Statistical Research Division, RR-88/07 (1988), http://www.census.gov/srd/papers/pdf/rr88-07.pdf
Brown, I.: An empirical comparison of constrained optimization methods for benchmarking economic time series. In: JSM Proceedings. Business and Economic Statistics Section. American Statistical Association, Alexandria (2010)
Causey, B., Trager, M.L.: Derivation of Solution to the Benchmarking Problem: Trend Revision. Unpublished research notes, U.S. Census Bureau, Washington D.C. (1981) Available as an appendix in Bozik and Otto (1988)
Cholette, P.A.: Adjusting sub-annual series to yearly benchmarks. Survey Methodol. 10, 35–49 (1984)
Dagum, E.B., Cholette, P.A.: Benchmarking, Temporal Distribution, and Reconciliation Methods for Time Series. Springer, New York (2006)
Denton, F.T.: Adjustment of monthly or quarterly series to annual totals: an approach based on quadratic minimization. JASA 333, 99–102 (1971)
Di Fonzo, T., Marini, M.: Benchmarking and movement preservation. Evidences from real-life and simulated series. Working Paper n. 14/2010, Department of Statistical Sciences, University of Padua (2010), http://www.stat.unipd.it/ricerca/fulltext?wp=422
Di Fonzo, T., Marini, M.: A Newton’s method for benchmarking time series according to a growth rates preservation principle. Working Paper n. 07/2011, Department of Statistical Sciences, University of Padua (2011), http://www.stat.unipd.it/ricerca/fulltext?wp=432
Mittelmann, H.D., Pruessner, A.: A server for automated performance analysis of benchmarking data. Optim. Methods Software 21, 105–120 (2006)
Schmidt, M.: The minFunc Toolbox for Matlab. March 15, 2011. http://www.cs.ubc.ca/\~schmidtm (2006)
The MathWorks: Optimization ToolboxTM4 User’s Guide. Natick, MA (2009)
Trager, M.L.: Derivation of Solution to the Benchmarking Problem: Relative Revision. Unpublished research notes, U.S. Census Bureau, Washington D.C., (1982) Available as an appendix in Bozik and Otto (1988)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Italia
About this chapter
Cite this chapter
Di Fonzo, T., Marini, M. (2013). A Newton’s Method for Benchmarking Time Series. In: Grigoletto, M., Lisi, F., Petrone, S. (eds) Complex Models and Computational Methods in Statistics. Contributions to Statistics. Springer, Milano. https://doi.org/10.1007/978-88-470-2871-5_9
Download citation
DOI: https://doi.org/10.1007/978-88-470-2871-5_9
Published:
Publisher Name: Springer, Milano
Print ISBN: 978-88-470-2870-8
Online ISBN: 978-88-470-2871-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)