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Hedonic Imputation versus Time Dummy Hedonic Indexes

In: Price Index Concepts and Measurement

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Listed:
  • 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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Suggested Citation

  • W. Erwin Diewert & Saeed Heravi & Mick Silver, 2009. "Hedonic Imputation versus Time Dummy Hedonic Indexes," NBER Chapters, in: Price Index Concepts and Measurement, pages 161-196, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:5073
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    References listed on IDEAS

    as
    1. Erwin Diewert, 2010. "Adjacent Period Dummy Variable Hedonic Regressions and Bilateral Index Number Theory," NBER Chapters, in: Contributions in Memory of Zvi Griliches, pages 759-786, National Bureau of Economic Research, Inc.
    2. Diewert, Erwin, 2007. "Index Numbers," Economics working papers diewert-07-01-03-08-17-23, Vancouver School of Economics, revised 31 Jan 2007.
    3. Mick Silver & Saeed Heravi, 2003. "The Measurement of Quality-Adjusted Price Changes," NBER Chapters, in: Scanner Data and Price Indexes, pages 277-316, National Bureau of Economic Research, Inc.
    4. 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.
    5. Feenstra, Robert C, 1995. "Exact Hedonic Price Indexes," The Review of Economics and Statistics, MIT Press, vol. 77(4), pages 634-653, November.
    6. Ariel Pakes, 2003. "A Reconsideration of Hedonic Price Indexes with an Application to PC's," American Economic Review, American Economic Association, vol. 93(5), pages 1578-1596, December.
    7. 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.
    8. 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.
    9. Ernst R. Berndt & Neal J. Rappaport, 2001. "Price and Quality of Desktop and Mobile Personal Computers: A Quarter-Century Historical Overview," American Economic Review, American Economic Association, vol. 91(2), pages 268-273, May.
    10. 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.
    11. Robert C. Feenstra & Matthew D. Shapiro, 2003. "Scanner Data and Price Indexes," NBER Books, National Bureau of Economic Research, Inc, number feen03-1, March.
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    More about this item

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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