Using Scanner Data to Construct CPI Basic Component Indexes

Reinsdorf, Marshall

doi:10.2307/1392470

Cited by 24 publications

(24 citation statements)

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“…Additionally, we used both unique product code and outlet combinations as the definition of a good as well as aggregating product codes across outlets . It was anticipated that this latter approach would increase the extent of matching and mitigate the effects of new and disappearing goods (Reinsdorf, 1999, p. 153). These various aggregation methods give four different approaches in total.…”

Section: An Empirical Application Using Scanner Datamentioning

confidence: 99%

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Accounting for the Effects of New and Disappearing Goods Using Scanner Data

Melser

2006

Review of Income and Wealth

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With the "discovery" of scanner data by statistical agencies and researchers comes a wealth of new information upon which price index calculations can be based. Old problems, such as the appearance and disappearance of goods over time, are likely to be an important feature of such data. However, given that scanner data includes the prices and quantities of the population of transactions we have more information than is traditionally available to deal with the new and disappearing goods problem. We adopt a recently developed approach using the Constant Elasticity of Substitution cost function to provide a detailed empirical analysis of the effects of new and disappearing goods for an Australian scanner data set of supermarket products. Our results indicate that the failure to account for new and disappearing goods in the cost-of-living index leads to a significant upward bias. Copyright � 2006 The Author; Journal compilation � International Association for Research in Income and Wealth 2006.

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Section: An Empirical Application Using Scanner Datamentioning

confidence: 99%

“…However, while this is a strong feature of the data used in this study it may not arise in all such data sets. For example, in Reinsdorf's coffee data (Reinsdorf, 1999, p. 155, Tab. 3) there seems to be no systematic difference between the expenditure on new and disappeared varieties of coffee.…”

Section: An Empirical Application Using Scanner Datamentioning

confidence: 99%

Accounting for the Effects of New and Disappearing Goods Using Scanner Data

Melser

2006

Review of Income and Wealth

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“…Reinsdorf (1999), for example, uses either monthly unit-values, or the prices in the third week of each month, to construct monthly price indexes for coffee. The collection of prices on a single day, which are then used to construct monthly indexes, corresponds to current practice at the Bureau of Labor Statistics.…”

Section: Introductionmentioning

confidence: 99%

High-Frequency Substitution and the Measurement of Price Indexes

Feenstra

Shapiro

2001

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“…Dalén (), Hawkes (), Ivancic et al . (), Jain and Abello (), Reinsdorf (), and Rotaru et al . () all compare a Laspeyres index with a Fisher ideal index.…”

Section: Related Literaturementioning

confidence: 98%

Elementary Index Bias: Evidence for the Euro Area from a Large Scanner Dataset

Gábor-Tóth

Vermeulen

2019

German Economic Review

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We provide evidence on the effect of elementary index choice on inflation measurement in the euro area. Using scanner data for 15,844 individual items from 42 product categories and 10 euro area countries, we compute product category level elementary price indexes using eight different elementary index formulas. Measured inflation outcomes of the different index formulas are compared with the Fisher ideal index to quantify elementary index bias. We have three main findings. First, elementary index bias is quite variable across product categories, countries and index formulas. Second, a comparison of elementary index formulas with and without expenditure weights shows that a shift from price only indexes to expenditure weighted indexes would entail at the product level multiple percentage points differences in measured price changes. And finally, we show that elementary index bias is quantitatively more important than upper level substitution bias.

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Using Scanner Data to Construct CPI Basic Component Indexes

Cited by 24 publications

References 0 publications

Accounting for the Effects of New and Disappearing Goods Using Scanner Data

Accounting for the Effects of New and Disappearing Goods Using Scanner Data

High-Frequency Substitution and the Measurement of Price Indexes

Elementary Index Bias: Evidence for the Euro Area from a Large Scanner Dataset

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