Statistics by subject – Prices and price indexes

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  • Articles and reports: 12-001-X19990014710
    Description:

    Most statistical offices select the sample of commodities of which prices are collected for their Consumer Price Indexes with non-probability techniques. In the Netherlands, and in many other countries as well, those judgemental sampling methods come close to some kind of cut-off selection, in which a large part of the population (usually the items with the lowest expenditures) is deliberately left unobserved. This method obviously yields biased price index numbers. The question arises whether probability sampling would lead to better results in terms of the mean square error. We have considered simple random sampling, stratified sampling and systematic sampling proportional to expenditure. Monte Carlo simulations using scanner data on coffee, baby's napkins and toilet paper were carried out to assess the performance of the four sampling designs. Surprisingly perhaps, cut-off selection is shown to be a successful strategy for item sampling in the consumer price index.

    Release date: 1999-10-08

  • Articles and reports: 12-001-X19980024351
    Description:

    To calculate price indexes, data on "the same item" (actually a collection of items narrowly defined) must be collected across time periods. The question arises whether such "quasi-longitudinal" data can be modeled in such a way as to shed light on what a price index is. Leading thinkers on price indexes have questioned the feasibility of using statistical modeling at all for characterizing price indexes. This paper suggests a simple state space model of price data, yielding a consumer price index that is given in terms of the parameters of the model.

    Release date: 1999-01-14

  • Articles and reports: 62F0014M19970103362
    Description:

    The debate on the measurement of bias in the CPI has been around for decades. However, given the size of government budgetary deficits, the issue of overestimating inflation and therefore payments in social benefits has triggered the interest in the measurement of the CPI bias. The final report of the U.S. Advisory Commission to Study the Consumer Price Index, chaired by Michael Boskin, states that the U.S. CPI has been overestimated by 1.1% per year since 1996. Following the release of the report, many interested groups have asked the question as to the magnitude of the bias for Canada's CPI. This result raised the question whether the bias in the Canadian CPI was of the same magnitude. This paper begins by presenting the bias issue in the context of the Canadian CPI and then outlines some of the plans Statistics Canada intends to undertake in the near future to improve the measurement of the CPI. The paper concludes that, although the Canadian CPI may suffer from the same potential problems as the U.S. CPI, the overall effect of these biases is less notable because Statistics Canada started to apply an appropriate methodology earlier. In fact, in recent studies Crawford (1993 and 1997) tried to estimate an overall bias and concluded that given the generous judgement incorporated in the estimate, it is likely that the bias is, on average smaller than 0.5%.

    Release date: 1997-10-02

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  • Articles and reports: 12-001-X19990014710
    Description:

    Most statistical offices select the sample of commodities of which prices are collected for their Consumer Price Indexes with non-probability techniques. In the Netherlands, and in many other countries as well, those judgemental sampling methods come close to some kind of cut-off selection, in which a large part of the population (usually the items with the lowest expenditures) is deliberately left unobserved. This method obviously yields biased price index numbers. The question arises whether probability sampling would lead to better results in terms of the mean square error. We have considered simple random sampling, stratified sampling and systematic sampling proportional to expenditure. Monte Carlo simulations using scanner data on coffee, baby's napkins and toilet paper were carried out to assess the performance of the four sampling designs. Surprisingly perhaps, cut-off selection is shown to be a successful strategy for item sampling in the consumer price index.

    Release date: 1999-10-08

  • Articles and reports: 12-001-X19980024351
    Description:

    To calculate price indexes, data on "the same item" (actually a collection of items narrowly defined) must be collected across time periods. The question arises whether such "quasi-longitudinal" data can be modeled in such a way as to shed light on what a price index is. Leading thinkers on price indexes have questioned the feasibility of using statistical modeling at all for characterizing price indexes. This paper suggests a simple state space model of price data, yielding a consumer price index that is given in terms of the parameters of the model.

    Release date: 1999-01-14

  • Articles and reports: 62F0014M19970103362
    Description:

    The debate on the measurement of bias in the CPI has been around for decades. However, given the size of government budgetary deficits, the issue of overestimating inflation and therefore payments in social benefits has triggered the interest in the measurement of the CPI bias. The final report of the U.S. Advisory Commission to Study the Consumer Price Index, chaired by Michael Boskin, states that the U.S. CPI has been overestimated by 1.1% per year since 1996. Following the release of the report, many interested groups have asked the question as to the magnitude of the bias for Canada's CPI. This result raised the question whether the bias in the Canadian CPI was of the same magnitude. This paper begins by presenting the bias issue in the context of the Canadian CPI and then outlines some of the plans Statistics Canada intends to undertake in the near future to improve the measurement of the CPI. The paper concludes that, although the Canadian CPI may suffer from the same potential problems as the U.S. CPI, the overall effect of these biases is less notable because Statistics Canada started to apply an appropriate methodology earlier. In fact, in recent studies Crawford (1993 and 1997) tried to estimate an overall bias and concluded that given the generous judgement incorporated in the estimate, it is likely that the bias is, on average smaller than 0.5%.

    Release date: 1997-10-02

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