Statistics by subject – Prices and price indexes

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  • Articles and reports: 62F0014M2001015

    The Canadian Consumer Price Index (CPI) applies a version of the user cost approach to measure the cost of home ownership. Because this approach specifically estimates the costs of using owned accommodation and not those faced by tenants, the measure includes a "replacement cost" (or depreciation) component. Depreciation is the only component in the CPI that is not an out-of-pocket expense. Consequently, economists face a unique set of methodological challenges when measuring depreciation.

    Between 1949 and 1997, the annual housing depreciation rate used in the CPI was 2%. Statistics Canada adopted the rate from a study that analysed U.S. Federal Housing Administration field appraisal data from 1939.

    This study argues that there is evidence that the 2% depreciation rate is too high to continue to use in the future. Consider that: 1) other Canadian studies show an upper bound of 1.7%, with a median estimate of 1.5%; 2) other statistical agencies use lower rates; and 3) every academic study over the past 40 years has arrived at a lower rate. As a consequence of this study and the existing supporting evidence, the depreciation rate in the Canadian CPI was lowered to 1.5% effective January 1998.

    Release date: 2001-11-28

  • Articles and reports: 62F0014M2001014

    This paper is the first in a series of reports examining the possible use of scanner data for constructing price indexes. This case study focuses on televisions and compares their price behaviour taken from current surveying methods with alternative measures obtained from massaging electronic data records on all sales by a retailer over a comparable period. Examination of the price index history for televisions shows that the recognition and adjustment for quality change in the sample and the impact of shifts in purchasing patterns have similar impact on the index numbers. The advantages of scanner data - that they record actual sales and current purchasing patterns - have to be set against the difficulty of recognising quality change. This analysis shows that while there are substantial gains from using scanner data in monitoring and adjusting for purchasing pattern changes, it is difficult to account for quality changes without micro-editing the data. The scanner data set raises statistical issues, largely questions of what aggregation across time, outlets and products should be done, that have to be answered before using it in index estimation. Future analysis will be aimed at resolving these issues.

    Release date: 2001-06-01

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