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FEATURE ARTICLE THE USE OF TRANSACTIONS DATA TO COMPILE THE AUSTRALIAN CONSUMER PRICE INDEX
ii. To reduce data collection costs; and
iii. To position the ABS to expand the range of consumer price measures.
i. Improving the accuracy of the CPI
The accuracy of the CPI is improved when transactions data are used to:
ii. Reducing data collection costs
Collecting ‘shelf’ prices by personal visit to businesses is a significant cost in producing the Australian CPI. Obtaining a consistent and timely supply of transactions data from businesses would reduce the work involved in data collection.
iii. To position the ABS to expand the range of consumer price measures
The ABS, subject to available funding, will examine transactions data to determine whether additional consumer price measures can be produced from transactions data. For example, regional and monthly temporal price indexes may be able to be produced from the data for selected product groups. Spatial price indexes may also be able to be produced.
Using transactions data in the CPI
Using transactions data to compile the CPI has received considerable attention from price statisticians internationally. Typical of very large datasets, there are challenges associated with their use. Various methods have been examined internationally over the previous decade to determine how best to utilise such a large volume of very detailed, high frequency data to compile the CPI. Most recently the ‘Rolling Window GEKS’ (RWGEKS)(footnote 2) approach has emerged as the leading methodology.
The RWGEKS method builds on the multilateral method of Gini (1931), Eltetö and Köves (1964) and Szulc (1964) (GEKS), which is known from spatial price comparisons and applies this method to price comparisons across time. The GEKS method takes the geometric mean of the ratios of all bilateral indexes (calculated using the same index number formula) between a number of entities. For spatial indexes these entities are generally countries, while for price comparisons across time, the entities are time periods (usually months).
A problem with this approach is that the results for all time periods will change when the observation period is extended and new data are added. By consequence index numbers will be subject to continuous revision, something which is unacceptable for a CPI. The RWGEKS approach was suggested to address this problem.
The RWGEKS approach uses a moving window to continuously update the observation period as data for new periods become available and calculates the price change between the two most recent periods, without the need to revise previous periods. This approach is undertaken as the earlier data in the sample become less and less relevant for later comparisons.
Work is still continuing to resolve some RWGEKS methodological challenges, including quality change associated with disappearing products and their replacements. At this time no single method to incorporate transactions data into the CPI has received international endorsement.
A small number of National Statistics Offices (NSOs) have utilised transactions data to compile their CPI. The use of transactions data varies from replacement of field collected prices(footnote 3) to implementation of new index construction methods(footnote 4) .
The ABS will continue to engage with the international community to contribute to the development of an internationally endorsed methodology to use a large volume of high frequency data to compile the CPI.
The ABS approach
The ABS will implement transactions data in the Australian CPI in two phases.
The first phase will commence on 1 January 2014. The ABS will replace field collected prices with prices derived from transactions data. ABS field officers will no longer personally visit businesses that have provided the ABS with transactions data.
The price for an individual product is calculated from the transactions data by dividing a product’s revenue by the quantity sold. This price is referred to as a product unit value and represents the price experienced by consumers over a period of weeks or months. A product’s unit value is more representative of prices paid by consumers over the reference period than point-in-time pricing.
Larger price samples for individual products will be available from transactions data. A unit value price for individual products will be collected from an increased number of business outlets in each capital city.
Product unit values obtained from transactions data will be used to compile a range of CPI Expenditure Class indexes. These Expenditure Classes are listed in Attachment 1.
In the second phase, the ABS will assess the suitability of an internationally developed transactions data methodology and implement it as appropriate to compile the CPI.
The ABS is currently examining the RWGEKS method. The ABS will produce experimental indexes using this methodology. Results of this work will be published and discussed with the user community.
ABS work has also commenced to determine whether transactions data can be used to compile a range of other macroeconomic statistics in the areas of retail activity and household consumption. Results of this work will be published as they become available.
Attachment 1: Expenditure Classes where some prices will be derived from transactions data
Eltetö, O. and P. Köves (1964) 'On a problem of index number computation relating to international comparison', Statisztikai Szemle, 42, pp. 507-18.
Gini C. (1931) 'On the circular test of index numbers', International Review of Statistics, Vol. 9.
Ivancic, L., W.E. Diewert and K.J. Fox (2011), Scanner Data, Time Aggregation and the Construction of Price Indexes, Journal of Econometrics, Vol. 161, Issue 1, 24-35.
Sammar, M, Norberg, A. and Tongur, C. (2013), ’Issues on the use of scanner data in the CPI’, Paper presented to the Thirteenth Meeting of the International Working Group on Price Indices, Copenhagen, Denmark.
Szulc B. (1964), 'Indices for multiregional comparisons', Przeglad Statystyezny 3, Statistical Review 3, pp. 239-54.
United Nations, International Monetary Fund, Organisation for Economic Co-operation and Development, World Bank and Commission of the European Communities, Brussels/Luxembourg, New York, Paris, Washington D.C., 2008 System of National Accounts.
van der Grient, H.A. and J. de Haan (2011), Scanner Data Price Indexes: The ’Dutch Method’ versus RYGEKS, Paper presented at the Twelfth Meeting of the International Working Group on Price Indices, Wellington, New Zealand.
1 2008 System of National Accounts, p39, [3.7] <back
2 Ivancic, L., W.E. Diewert and K.J. Fox (2011), Scanner Data, Time Aggregation and the Construction of Price Indexes, Journal of Econometrics, Vol. 161, Issue 1, 24-35 <back
3 Sammar, M, Norberg, A. and Tongur, C. (2013),’ Issues on the use of scanner data in the CPI’, Paper presented to the Thirteenth Meeting of the International Working Group on Price Indices, Copenhagen, Denmark <back
4 van der Grient, H.A. and J. de Haan (2011), Scanner Data Price Indexes: The ’Dutch Method’ versus RYGEKS, Paper presented at the Twelfth Meeting of the International Working Group on Price Indices, Wellington, New Zealand. <back
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