AbstractRecent increases in energy prices,

This article develops a statistical model-based approach to the benchmarking problem. Benchmarking is done when data from a monthly sample survey are combined with data from an annual census for the purpose of improving the survey estimates. Previous authors have used numerical analysis techniques to derive methods to perform benchmarking. This article formulates the benchmarking problem in a statistical framework and uses modern times series methods to derive a solution. This solution is based in part upon the statistical properties of the time series being benchmarked and upon the properties of the survey errors associated with that time series. The article makes use of the theory of signal extraction that has been derived for nonstationary time series. Two common types of benchmarking problems are studied in greater detail. The results of the theory derived in the article are illustrated by an example.

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