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A Comparison of Weighted Time-Product Dummy and Time Dummy Hedonic Indexes

Listed author(s):
  • Jan de Haan

    ()

    (Division of Corporate Services, IT and Methodology, Statistics Netherlands)

  • Rens Hendriks

    ()

    (Statistics for Development Division, Pacific Community (SPC))

  • Michael Scholz

    ()

    (University of Graz)

Registered author(s):

    This paper compares two model-based multilateral price indexes: the time- product dummy (TPD) index and the time dummy hedonic (TDH) index, both estimated by expenditure-share weighted least squares regression. The TPD model can be viewed as the saturated version of the underlying TDH model, and we argue that the regression residuals are ``distorted towards zero'' due to overfitting. We decompose the ratio of the two indexes in terms of average regression residuals of the new and disappearing items (plus a third component that depends on the change in the matched items' normalized expenditure shares). The decomposition explains under which conditions the TPD index suffers from quality-change bias or, more generally, lack-of-matching bias. An example using scanner data on men's t-shirts illustrates our theoretical framework.

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    File URL: http://www100.uni-graz.at/vwlwww/forschung/RePEc/wpaper/2016-13.pdf
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    Paper provided by University of Graz, Department of Economics in its series Graz Economics Papers with number 2016-13.

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    Date of creation: Nov 2016
    Handle: RePEc:grz:wpaper:2016-13
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    University of Graz, Universitaetsstr. 15/F4, 8010 Graz, Austria

    Phone: ++43 316 380-3440
    Fax: ++43 316 380-9520, 9521
    Web page: http://volkswirtschaftslehre.uni-graz.at/
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    1. Mark Bils, 2009. "Do Higher Prices for New Goods Reflect Quality Growth or Inflation?," The Quarterly Journal of Economics, Oxford University Press, vol. 124(2), pages 637-675.
    2. Ivancic, Lorraine & Erwin Diewert, W. & Fox, Kevin J., 2011. "Scanner data, time aggregation and the construction of price indexes," Journal of Econometrics, Elsevier, vol. 161(1), pages 24-35, March.
    3. 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.
    4. Silver, Mick & Heravi, Saeed, 2005. "A Failure in the Measurement of Inflation: Results From a Hedonic and Matched Experiment Using Scanner Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 269-281, July.
    5. Erwin Diewert, 2005. "Weighted Country Product Dummy Variable Regressions And Index Number Formulae," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 51(4), pages 561-570, December.
    6. Kevin J. Fox & Daniel Melser, 2014. "Non-Linear Pricing and Price Indexes: Evidence and Implications from Scanner Data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 60(2), pages 261-278, June.
    7. Summers, Robert, 1973. "International Price Comparisons Based Upon Incomplete Data," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 19(1), pages 1-16, March.
    8. Reinsdorf, Marshall B, 1999. "Using Scanner Data to Construct CPI Basic Component Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(2), pages 152-160, April.
    9. de Haan, Jan & van der Grient, Heymerik A., 2011. "Eliminating chain drift in price indexes based on scanner data," Journal of Econometrics, Elsevier, vol. 161(1), pages 36-46, March.
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