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The reconciliation of systems of time series subject to both temporal and contemporaneous constraints can be solved in such a way that the temporal profiles of the original series be preserved ‘at best’ (the movement preservation principle). A new feasible simultaneous reconciliation procedure is presented, which exploits the sparsity of the linear system to be solved. A two-step reconciliation strategy might be more suitable in the case of large systems.We compare the results of the simultaneous and two-step approaches for two data sets from real life

When a system of time series is seasonally adjusted, generally the accounting constraints originally linking the series are not fulfilled. To overcome this problem, we discuss an extension to a system of series linked by an accounting constraint of the classical univariate benchmarking procedure due to Denton (1971), which is founded on a movement preservation principle that is very relevant in this case. The presence of linear dependence between the variables makes it necessary to deal with the whole set of contemporaneous and temporal aggregation relationships. The cases of one-way classified (e.g., by regions or by industries) and of two-way classified (e.g., by regions and by industries) systems of series are studied. An empirical application to the Canadian retail trade series by province (12 series) and trade groups (18 series) is considered to show the capability of the proposed procedures

We model the electricity consumption in the market segment that compose the Qatari electricity market. We link electricity consumption to GDP growth and Population Growth. Building on the estimated model, we develop long-range forecasts of electricity consumption from 2017 to 2030 over different scenarios for the economic drivers. In addition, we proxy for electricity efficiency improvements by reducing the long-run elasticity of electricity consumption to GDP and Population. We show that electricity efficiency has a crucial role in controlling the future development of electricity consumption. Energy policies should consider this aspect and support both electricity efficiency improvement programs, as well as a price reform.

We present a new technique for temporally benchmarking a time series according to a Growth Rates Preservation (GRP) principle. This procedure basically looks for the solution to a non linear program, according to which a smooth, non-convex function of the unknown values of the target time series has to be minimized subject to linear equality constraints which link the more frequent series to a given, less frequent benchmark series. We develop a Newton's method with Hessian modification applied to a suitably reduced-unconstrained problem. This method exploits the analytic Hessian of the GRP objective function, making full use of all the derivative information at disposal. 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 currently used by statistical agencies.