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An Experimental Component Index for the CPI: From Annual Computer Data to Monthly Data on Other Goods

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  • Timothy Erickson
  • Ariel Pakes

Abstract

Until recently the Consumer Price Index consisted solely of "matched model" component indexes. The latter are constructed by BLS personnel who visit stores and compare prices of goods with the same set of characteristics over successive periods. This procedure is subject to a selection bias. Goods that were not on the shelves in the second period were discarded and hence never contributed price comparisons. The discarded goods were disproportionately goods which were being obsoleted and had falling prices. Pakes (2003) provided an analytic framework for analyzing this selection effect and showed both that it could be partially corrected using a particular hedonic technique and that the correction for his personal computer example was substantial. The BLS staff has recently increased the rate at which they incorporate techniques to correct for selection effects in their component indexes. However recent work shows very little difference between hedonic and matched model indices for non computer components of the CPI. This paper explores why. We look carefully at the data on the component index for TVs and show that differences between the TV and computer markets imply that to obtain an effective selection correction we need to use a more general hedonic procedure than has been used to date. The computer market is special in having well defined cardinal measures of the major product characteristics. In markets where such measures are absent we may need to allow for selection on unmeasured, as well as measured, characteristics. We develop a hedonic selection correction that accounts for unmeasured characteristics, apply it to TVs, and show that it yields a much larger selection correction than the standard hedonic. In particular we find that matched model techniques underestimate the rate of price decline by over 20%.

Suggested Citation

  • Timothy Erickson & Ariel Pakes, 2008. "An Experimental Component Index for the CPI: From Annual Computer Data to Monthly Data on Other Goods," NBER Working Papers 14368, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:14368
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    1. Newey, Whitney K, 1994. "The Asymptotic Variance of Semiparametric Estimators," Econometrica, Econometric Society, vol. 62(6), pages 1349-1382, November.
    2. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
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    Cited by:

    1. Daniel Melser & Iqbal A. Syed, 2016. "Life Cycle Price Trends and Product Replacement: Implications for the Measurement of Inflation," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 62(3), pages 509-533, September.
    2. Clemens C Struck & Clemens C. Struck, 2017. "On the Interaction of Growth, Trade and International Macroeconomics," Working Papers 201724, School of Economics, University College Dublin.
    3. Xosé-Luís Varela-Irimia, 2014. "Age effects, unobserved characteristics and hedonic price indexes: The Spanish car market in the 1990s," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 5(4), pages 419-455, November.
    4. Iqbal A. Syed & Jan De Haan, 2017. "Age, Time, Vintage, And Price Indexes: Measuring The Depreciation Pattern Of Houses," Economic Inquiry, Western Economic Association International, vol. 55(1), pages 580-600, January.
    5. Klenow, Peter J. & Li, Huiyu, 2017. "Missing Growth from Creative Destruction," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    6. Emi Nakamura & Jón Steinsson, 2012. "Lost in Transit: Product Replacement Bias and Pricing to Market," American Economic Review, American Economic Association, vol. 102(7), pages 3277-3316, December.
    7. Ana Aizcorbe & Nicole Nestoriak, 2010. "Price Indexes for Drugs: A Review of the Issues," BEA Working Papers 0050, Bureau of Economic Analysis.
    8. Aizcorbe, Ana & Bridgman, Benjamin & Nalewaik, Jeremy, 2010. "Heterogeneous car buyers: A stylized fact," Economics Letters, Elsevier, vol. 109(1), pages 50-53, October.
    9. Adam Hale Shapiro & Ana Aizcorbe, 2010. "Implications of Consumer Heterogeneity on Price Measures for Technology Goods," BEA Working Papers 0062, Bureau of Economic Analysis.
    10. Adam Copeland & Adam Hale Shapiro, 2010. "The impact of competition on technology adoption: an apples-to-PCs analysis," Staff Reports 462, Federal Reserve Bank of New York.
    11. Copeland, Adam & Shapiro, Adam Hale, 2013. "Price Setting in an Innovative Market," Working Paper Series 2013-04, Federal Reserve Bank of San Francisco.

    More about this item

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L16 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Industrial Organization and Macroeconomics; Macroeconomic Industrial Structure

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