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Hedonic Imputation versus Time Dummy Hedonic Indexes

In: Price Index Concepts and Measurement

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  • W. Erwin Diewert
  • Saeed Heravi
  • Mick Silver

Abstract

Statistical offices try to match item models when measuring inflation between two periods. However, for product areas with a high turnover of differentiated models, the use of hedonic indexes is more appropriate since they include unmatched new and old models. There are two main competing approaches to hedonic indexes are hedonic imputation (HI) indexes and dummy time hedonic (HD) indexes. This study provides a formal analysis of exactly why the results from the two approaches may differ and discusses the issue of choice between these approaches. An illustrative study for desktop PCs is provided.

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This chapter was published in:

  • W. Erwin Diewert & John S. Greenlees & Charles R. Hulten, 2009. "Price Index Concepts and Measurement," NBER Books, National Bureau of Economic Research, Inc, number diew08-1, octubre-d.
    This item is provided by National Bureau of Economic Research, Inc in its series NBER Chapters with number 5073.

    Handle: RePEc:nbr:nberch:5073

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    References

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    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    1. W. Erwin Diewert, 2003. "Hedonic Regressions. A Consumer Theory Approach," NBER Chapters, in: Scanner Data and Price Indexes, pages 317-348 National Bureau of Economic Research, Inc.
    2. Robert C. Feenstra & Matthew D. Shapiro, 2003. "Scanner Data and Price Indexes," NBER Books, National Bureau of Economic Research, Inc, number feen03-1, octubre-d.
    3. Jack Triplett, 2004. "Handbook on Hedonic Indexes and Quality Adjustments in Price Indexes: Special Application to Information Technology Products," OECD Science, Technology and Industry Working Papers 2004/9, OECD Publishing.
    4. Robert C. Feenstra, 1995. "Exact Hedonic Price Indexes," NBER Working Papers 5061, National Bureau of Economic Research, Inc.
    5. Ariel Pakes, 2002. "A Reconsideration of Hedonic Price Indices with an Application to PC's," NBER Working Papers 8715, National Bureau of Economic Research, Inc.
    6. 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.
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    Cited by:
    1. W. Diewert, 2011. "Measuring productivity in the public sector: some conceptual problems," Journal of Productivity Analysis, Springer, vol. 36(2), pages 177-191, October.
    2. Raquel Arévalo Tomé & José María Chamorro Rivas, . "Geographic Heterogeneity in Housing. Evidence from Spain," Studies on the Spanish Economy 203, FEDEA.
    3. Robert J. Hill & Daniel Melser, 2007. "Comparing House Prices Across Regions and Time: An Hedonic Approach," Discussion Papers 2007-33, School of Economics, The University of New South Wales.
    4. Mick Silver, 2009. "The Hedonic Country Product Dummy Method and Quality Adjustments for Purchasing Power Parity Calculations," IMF Working Papers 09/271, International Monetary Fund.
    5. 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.
    6. Brachinger, Hans Wolfgang & Beer, Michael, 2009. "The Econometric Foundations of Hedonic Elementary Price Indices," DQE Working Papers 12, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland.

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