5514.0.55.001 - Australian System of Government Finance Statistics: Concepts, Sources and Methods, 2003  
ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 10/10/2003   
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4.1. Chapter 3 described the sources of GFS data and the data collection methods used. This chapter describes the next step in data processing, namely the processes of classifying, editing and consolidating the collected data to create the statistics.

4.2. The description of the compilation methodology in this chapter is targeted more towards users of the statistics than compilers. It provides a broad overview rather than a detailed description of particular procedural or operational steps. Processes are described in logical terms that do not necessarily reflect the physical structure of the computer systems underlying the processes.


4.3. GFS compilation involves transformation of the accounting data of public sector units into economic statistics. This is achieved through identification and classification of the units and analysis, classification and consolidation of economic flows and stocks recorded in the units’ accounting records. The following sequence of processes is involved:

  • GFS classification of units;
  • GFS classification of flows and stocks;
  • creation of an input data base containing unit level data;
  • input editing of unit level data;
  • data aggregartion, consolidation and derivation;
  • estimation on a quarterly basis only;
  • output editing of the data;
  • creation of an output data base containing aggregated data (used for dissemination of the statistics).

4.4 These processes are shown below in the form of a flow chart.

Graphic - Chart 5.1: Schematic overview of GFS compilation process

4.5. The compilation processes identified in the previous paragraph apply to all GFS data phases described in chapter 3 (i.e. quarterly estimates, forward annual estimates, preliminary annual estimates, and final annual data). However, estimation applies only to quarterly data. This is because quarterly statistics are compiled using a mixture of full enumeration and sampling, which requires an additional process of expanding the sample data to provide estimates for the component not covered by the sample.

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