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Hedonic Imputation Versus Time Dummy Hedonic Indexes

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  • W. E. Diewert
  • Mick Silver
  • Saeed Heravi

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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Bibliographic Info

Paper provided by International Monetary Fund in its series IMF Working Papers with number 07/234.

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Length: 36
Date of creation: 01 Oct 2007
Date of revision:
Handle: RePEc:imf:imfwpa:07/234

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Keywords: Consumer price indexes; Economic models; equations; expenditure; expenditure share; equation; covariance; arithmetic; dummy variable; statistics; standard errors; standard error; constant term; number theory; constant variance; minimization; dummy variables; diagonal matrix; covariances; parameter estimate; sampling; random variables; least squares regression; econometrics; outliers; logarithm; survey; orthogonality; logarithms; sample selection; algebra; functional form; explanatory power; functional forms;

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References

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  1. 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.
  2. Robert C. Feenstra & Matthew D. Shapiro, 2003. "Scanner Data and Price Indexes," NBER Books, National Bureau of Economic Research, Inc, number feen03-1.
  3. Feenstra, R.C., 1995. "Exact Hedonic Price Indexes," Papers 95-11, California Davis - Institute of Governmental Affairs.
  4. 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.
  5. 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.
  6. 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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Cited by:
  1. 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.
  2. 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.
  3. Raquel Arévalo Tomé & José María Chamorro Rivas, . "Geographic Heterogeneity in Housing. Evidence from Spain," Studies on the Spanish Economy 203, FEDEA.
  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. 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. 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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