No abstract

The generalized multivariate Denton model for achieving consistency between large accounting frameworks developed at Statistics Netherlands (SN) is originally intended for benchmarking of supply and use (SU) tables of national accounts. The success of this application in the production process triggered using the model for other processes within the office. Currently, at SN, many production processes of national accounts, but also other departments, use the modifications of this optimization model for achieving consistency of data obtained from different sources. These include reconciliation and benchmarking of SU tables and of institutional sector accounts, ESA (European system of accounts), 1 revisions of SU tables, benchmarking of gross fixed capital formation, Population Census tables, and energy statistics figures. The mathematical model is based on a quadratic optimization function and combines different features, such as linear constraints, ratio constraints, weights, soft and hard constraints, and inequalities. The optimization problems we deal with can be very large, consisting of 500,000 variables and over 100,000 constraints. This optimization problem is solved using the commercially available software tool XPRESS and the free software tool R. For the reconciliation of trade and transport statistics, similar optimization techniques are used. In this paper, we give an overview of production processes at SN using macro-integration techniques.

Abstract. Macro-integration techniques are used for the reconciliation of macro figures, usually in the form of large multidimensional tabulations, obtained from different sources. Traditionally these techniques have been extensively applied in the area of macro-economics, especially in the compilation of the National Accounts. Methods for macro-integration have developed over the years to become very versatile techniques for integration of data from different sources at the macro level. Applications in other domains than macro-economics seem promising. In this paper we present an application to labour market data from two sources, an administrative one and a survey, with slightly different definitions and different frequencies of reporting (monthly, quarterly). The purpose is to combine these estimates to form a single monthly estimate. Depending on the specification of the macro-integration model several alternatives for obtaining such estimates are derived.

The problem of adjusting economic or social accounts can be quite complex when large accounting equation systems are considered. This is especially true if they must fulfill predefined, known functional relationships. For such complex systems, evaluating the accuracy of the estimates after the adjustment is difficult since they are defined by unadjusted initial estimates, the accounting equations and the adjustment method. In this paper, we consider such systems as a single entity and develop scalar uncertainty measures that capture the adjustment effect as well as the relative contribution of the various input estimates to the final estimated account. The scalar measures are based on the first two moments of the joint distribution of the underlying true accounting system without requiring specification of the distribution in full. Scalar measures can help to effectively communicate to the users the relevant uncertainty of disseminated macro-economic accounts, and can assist the producer in choosing and improving adjustment method and input estimators. The proposed approach will be illustrated both analytically and by simulation. Applications to supply and use tables and to time series data will be presented.

Macro-integration technique is a well established method for<br />reconciliation of large, high-dimensional tables, especially applied to macroeconomic data at national statistical oces (NSO). This technique is mainly used when data obtained from dierent sources should be reconciled on a macro level. New areas of applications for this technique arise as new data sources become available to NSO's. Often these new data sources cannot be combined on a micro level, while macro integration could provide a solution for such problems. Yet, more research should be carried out to investigate if in such situations macro integration could indeed be applied. In this paper we propose two applications of macro-integration techniques in other domains than the traditional macro-economic applications. In particular: reconciliation of tables of a virtual census and reconciliation of monthly series of short term statistics gures with the quarterly gures of structural business statistics.