The Product Life Cycle and Sample Representativity Bias in Price Indexes

21 Pages Posted: 30 Jun 2016

See all articles by Daniel Melser

Daniel Melser

RMIT University; Financial Research Network (FIRN)

Iqbal A. Syed

UNSW Australia Business School, School of Economics

Date Written: May 7, 2016

Abstract

Official price indexes are usually calculated using matched samples of products. If products exhibit systematic price trends at different points in their life cycle then matched sample methods may introduce bias if the life cycle movement in the sample does not adequately reflect that in the population. This article explores the extent of these life cycle pricing effects and then examines the bias it can introduce in measured inflation. A large US supermarket scanner data set for 6 cities and 6 products over 12 years is used. Using hedonic methods we find that the life cycle component of price change is important across a range of products and cities. To explore the bias introduced by these movements we use simulations which construct indexes with different sample update frequency. For indexes which are never completely re-sampled we find an annual bias of 0.88 and 0.59 percentage points depending upon whether we use the actual prices or prices imputed from our hedonic model. This compares with absolute biases of 0.24 and 0.08 percentage points for the corresponding cases for samples which are re-selected annually. Thus our results provide strong support for more frequently updating index samples.

Keywords: Consumer Price Index (CPI), Lifecycle Pricing, Hedonic Regression, Survey Sampling

JEL Classification: C43, E31

Suggested Citation

Melser, Daniel and Syed, Iqbal A., The Product Life Cycle and Sample Representativity Bias in Price Indexes (May 7, 2016). UNSW Business School Research Paper No. 2016-07. Available at SSRN: https://ssrn.com/abstract=2801890 or http://dx.doi.org/10.2139/ssrn.2801890

Daniel Melser (Contact Author)

RMIT University ( email )

124 La Trobe Street
Melbourne, 3000
Australia

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

Iqbal A. Syed

UNSW Australia Business School, School of Economics ( email )

High Street
Sydney, NSW 2052
Australia

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