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Reconciliation of systems of time series according to a growth rates preservation principle

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  • Tommaso Fonzo
  • Marco Marini

Abstract

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. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Tommaso Fonzo & Marco Marini, 2015. "Reconciliation of systems of time series according to a growth rates preservation principle," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 651-669, November.
    • Handle: RePEc:spr:stmapp:v:24:y:2015:i:4:p:651-669
    DOI: 10.1007/s10260-015-0322-y
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    References listed on IDEAS

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    1. J. Joseph Beaulieu & Eric J. Bartelsman, 2006. "Integrating Expenditure and Income Data: What to Do with the Statistical Discrepancy?," NBER Chapters, in: A New Architecture for the US National Accounts, pages 309-354, National Bureau of Economic Research, Inc.
    2. Tommaso Di Fonzo & Marco Marini, 2011. "Simultaneous and two‐step reconciliation of systems of time series: methodological and practical issues," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 60(2), pages 143-164, March.
    3. Quenneville, B. & Fortier, S. & Gagné, C., 2009. "A non-parametric iterative smoothing method for benchmarking and temporal distribution," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3386-3396, July.
    4. B. Quenneville & F. Picard & S. Fortier, 2013. "Calendarization with interpolating splines and state space models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(3), pages 371-399, May.
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    Cited by:

    1. Baoline Chen & Tommaso Di Fonzo & Thomas Howells & Marco Marini, 2018. "The statistical reconciliation of time series of accounts between two benchmark revisions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 533-552, November.

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