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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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  • Tim Erickson
  • Ariel Pakes

Abstract

The CPI component indices are obtained from comparing price quotes at a given store in different periods. If we omit comparisons from goods in the store in the initial, but not in the comparison, period we generate a selection bias: goods that exit are disproportionately obsolete goods that have falling prices. Building on Pakes (2003), we explain why standard hedonic predictions for second-period prices of exiting goods do not account for this bias. New hedonic methods are derived, shown to have desirable properties, and are applied to three CPI samples where they generate significant selection corrections. (JEL C43, E31)

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  • Tim Erickson & Ariel Pakes, 2011. "An Experimental Component Index for the CPI: From Annual Computer Data to Monthly Data on Other Goods," American Economic Review, American Economic Association, vol. 101(5), pages 1707-1738, August.
  • Handle: RePEc:aea:aecrev:v:101:y:2011:i:5:p:1707-38
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    1. Andrews, Donald W K, 1994. "Asymptotics for Semiparametric Econometric Models via Stochastic Equicontinuity," Econometrica, Econometric Society, vol. 62(1), pages 43-72, January.
    2. Newey, Whitney K, 1994. "The Asymptotic Variance of Semiparametric Estimators," Econometrica, Econometric Society, vol. 62(6), pages 1349-1382, November.
    3. 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.
    4. Patrick Bajari & C. Lanier Benkard, 2005. "Demand Estimation with Heterogeneous Consumers and Unobserved Product Characteristics: A Hedonic Approach," Journal of Political Economy, University of Chicago Press, vol. 113(6), pages 1239-1276, December.
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    9. Erica L. Groshen & Brian C. Moyer & Ana M. Aizcorbe & Ralph Bradley & David M. Friedman, 2017. "How Government Statistics Adjust for Potential Biases from Quality Change and New Goods in an Age of Digital Technologies: A View from the Trenches," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 187-210, Spring.
    10. 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.
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    13. Rojas, Christian & Jaenicke, Edward C. & Page, Elina T., 2024. "Shrinkflation? Quantifying the impact of changes in package size on food inflation," 2024 Annual Meeting, July 28-30, New Orleans, LA 343770, Agricultural and Applied Economics Association.
    14. Ana Aizcorbe & Nicole Nestoriak, 2010. "Price Indexes for Drugs: A Review of the Issues," BEA Working Papers 0050, Bureau of Economic Analysis.
    15. Aizcorbe, Ana & Bridgman, Benjamin & Nalewaik, Jeremy, 2010. "Heterogeneous car buyers: A stylized fact," Economics Letters, Elsevier, vol. 109(1), pages 50-53, October.
    16. Adam Hale Shapiro & Ana Aizcorbe, 2010. "Implications of Consumer Heterogeneity on Price Measures for Technology Goods," BEA Working Papers 0062, Bureau of Economic Analysis.
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    18. Laurien Gilbert, 2018. "Gains from Product Variety and the Local Business Cycle," 2018 Meeting Papers 46, Society for Economic Dynamics.
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    22. Struck, Clemens C., 2022. "Wealth, price levels, and product quality," International Economics, Elsevier, vol. 170(C), pages 32-48.

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    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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