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Hedonic Imputation versus Time Dummy Hedonic Indexes

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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 the prices and quantities of unmatched new and old models. The two main approaches to hedonic indexes are hedonic imputation (HI) indexes and dummy time hedonic (HD) indexes. This study provides a formal analysis of the difference between the two approaches for alternative implementations of an index that uses weighting that is comparable to the weighting used by the Törnqvist superlative index in standard index number theory. This study shows exactly why the results may differ and discusses the issue of choice between these approaches. An illustrative study for desktop PCs is provided.

Suggested Citation

  • W. Erwin Diewert & Saeed Heravi & Mick Silver, 2008. "Hedonic Imputation versus Time Dummy Hedonic Indexes," NBER Working Papers 14018, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:14018
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    References listed on IDEAS

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    1. Feenstra, Robert C, 1995. "Exact Hedonic Price Indexes," The Review of Economics and Statistics, MIT Press, vol. 77(4), pages 634-653, November.
    2. 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.
    3. 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.
    4. Diewert, Erwin, 2007. "Index Numbers," Economics working papers diewert-07-01-03-08-17-23, Vancouver School of Economics, revised 31 Jan 2007.
    5. 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.
    6. Robert C. Feenstra & Matthew D. Shapiro, 2003. "Scanner Data and Price Indexes," NBER Books, National Bureau of Economic Research, Inc, number feen03-1, July.
    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.
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    Citations

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    Cited by:

    1. 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.
    2. W. Diewert, 2011. "Measuring productivity in the public sector: some conceptual problems," Journal of Productivity Analysis, Springer, vol. 36(2), pages 177-191, October.
    3. Hussein, Mohamud & Fraser, Iain & Costanigro, Marco, 2016. "Hedonic Analysis of Origin of Meat In The United Kingdom," 90th Annual Conference, April 4-6, 2016, Warwick University, Coventry, UK 236353, Agricultural Economics Society.
    4. Scott, Alex, 2015. "The value of information sharing for truckload shippers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 81(C), pages 203-214.
    5. 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.
    6. Robert Leszczynski & Krzysztof Olszewski, 2015. "Commercial property price index for Poland," Bank i Kredyt, Narodowy Bank Polski, vol. 46(6), pages 565-578.
    7. Füss, Roland & Koller, Jan A., 2016. "The role of spatial and temporal structure for residential rent predictions," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1352-1368.
    8. GLUMAC Brano & DES ROSIERS François, 2018. "Real estate and land property automated valuation systems: A taxonomy and conceptual model," LISER Working Paper Series 2018-09, LISER.
    9. Raquel ArÈvalo TomÈ & JosÈ MarÌa Chamorro Rivas, "undated". "Geographic Heterogeneity in Housing. Evidence from Spain," Studies on the Spanish Economy 203, FEDEA.
    10. Shimizu, Chihiro & Karato, Koji, 2016. "Property Price Index Theory and Estimation: A Survey," HIT-REFINED Working Paper Series 34, Institute of Economic Research, Hitotsubashi University.
    11. 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.
    12. 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.

    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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