caa a22 4500 606196552 CHVBK 20210128100925.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s10260-015-0322-y doi (NATIONALLICENCE)springer-10.1007/s10260-015-0322-y Reconciliation of systems of time series according to a growth rates preservation principle [Elektronische Daten] [Tommaso Di Fonzo, Marco Marini] We propose new simultaneous and two-step procedures for reconciling systems of time series subject to temporal and contemporaneous constraints according to a growth rates preservation (GRP) principle. The techniques exploit the analytic gradient and Hessian of the GRP objective function, making full use of all the derivative information at disposal. We apply the new GRP procedures to two systems of economic series, and compare the results with those of reconciliation procedures based on the proportional first differences (PFD) principle, widely used by data-producing agencies. Our experiments show that (1) the nonlinear GRP problem can be efficiently solved through an interior-point optimization algorithm, and (2) GRP-based procedures preserve better the growth rates than PFD solutions, especially for series with high temporal discrepancy and high volatility. Springer-Verlag Berlin Heidelberg, 2015 Benchmarking nationallicence Reconciliation nationallicence Movement preservation nationallicence Nonlinear optimization nationallicence Di Fonzo Tommaso Department of Statistical Sciences, University of Padova, Via C. Battisti, 241, 35121, Padua, Italy aut Marini Marco International Monetary Fund, Washington, DC, USA aut Statistical Methods & Applications Springer Berlin Heidelberg 24/4(2015-11-01), 651-669 1618-2510 24:4<651 2015 24 10260 https://doi.org/10.1007/s10260-015-0322-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10260-015-0322-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Di Fonzo Tommaso Department of Statistical Sciences, University of Padova, Via C. Battisti, 241, 35121, Padua, Italy aut NATIONALLICENCE 700 1- Marini Marco International Monetary Fund, Washington, DC, USA aut NATIONALLICENCE 773 0- Statistical Methods & Applications Springer Berlin Heidelberg 24/4(2015-11-01), 651-669 1618-2510 24:4<651 2015 24 10260