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Hedonic Imputation versus Time Dummy Hedonic Indexes

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  • 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.

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Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 14018.

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Date of creation: May 2008
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Publication status: published as Hedonic Imputation versus Time Dummy Hedonic Indexes , W. Erwin Diewert, Saeed Heravi, Mick Silver. in Price Index Concepts and Measurement , Diewert, Greenlees, and Hulten. 2009
Handle: RePEc:nbr:nberwo:14018

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  1. 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.
  2. 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.
  3. Feenstra, R.C., 1995. "Exact Hedonic Price Indexes," Papers 95-11, California Davis - Institute of Governmental Affairs.
  4. Silver, Mick & Heravi, Saeed, 2002. "A failure in the measurement of inflation: results from a hedonic and matched experiment using scanner data," Working Paper Series 0144, European Central Bank.
  5. 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.
  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.
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Cited by:
  1. Diewert, Erwin, 2010. "Measuring Productivity in the Public Sector: Some Conceptual Problems," Economics working papers erwin_diewert-2010-6, Vancouver School of Economics, revised 13 Jul 2010.
  2. 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.
  3. 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.
  4. 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.
  5. Raquel Arévalo Tomé & José María Chamorro Rivas, . "Geographic Heterogeneity in Housing. Evidence from Spain," Studies on the Spanish Economy 203, FEDEA.
  6. 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.

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