Benchmarking and Reconciliation of Time Series according to a Growth Rates Preservation Principle

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 (i) the nonlinear GRP problem can be efficiently solved through Newton’s optimization algorithms, and (ii) GRP-based procedures preserve better the growth rates than PFD solutions, especially for series with high temporal discrepancy and high volatility.