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The Difference Between Hedonic Imputation Indexes and Time Dummy Hedonic Indexes

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  • Silver, Mick
  • Heravi, Saeed

Abstract

Statistical offices try to match item models when measuring inflation between two periods. For product areas with a high turnover of differentiated models, however, 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 (DTH) indexes. This study provides a formal analysis of the difference between the two approaches for alternative implementations of the Törnqvist "superlative" index. It shows why the results may differ and discusses the issue of choice between these approaches.
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  • Silver, Mick & Heravi, Saeed, 2007. "The Difference Between Hedonic Imputation Indexes and Time Dummy Hedonic Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 239-246, April.
    • Handle: RePEc:bes:jnlbes:v:25:y:2007:p:239-246
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    3. Esmeralda A. Ramalho & Joaquim J.S. Ramalho, 2014. "Convenient links for the estimation of hedonic price indexes: the case of unique, infrequently traded assets," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(2), pages 91-117, May.
    4. Robert J. Hill & Michael Scholz, 2014. "Incorporating Geospatial Data in House Price Indexes: A Hedonic Imputation Approach with Splines," Graz Economics Papers 2014-05, University of Graz, Department of Economics.
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    6. Diewert, Erwin, 2019. "Quality Adjustment and Hedonics: A Unified Approach," Microeconomics.ca working papers erwin_diewert-2019-2, Vancouver School of Economics, revised 14 Mar 2019.
    7. Erica L. Groshen & Brian C. Moyer & Ana M. Aizcorbe & Ralph Bradley & David M. Friedman, 2017. "How Government Statistics Adjust for Potential Biases from Quality Change and New Goods in an Age of Digital Technologies: A View from the Trenches," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 187-210, Spring.
    8. Robert J. Hill & Alicia N. Rambaldi & Michael Scholz, 2021. "Higher frequency hedonic property price indices: a state-space approach," Empirical Economics, Springer, vol. 61(1), pages 417-441, July.
    9. Agarwal, Sumit & Chua, Yeow Hwee & Song, Changcheng, 2022. "Inflation expectations of households and the upgrading channel," Journal of Monetary Economics, Elsevier, vol. 128(C), pages 124-138.
    10. Iqbal A. Syed & Jan De Haan, 2017. "Age, Time, Vintage, And Price Indexes: Measuring The Depreciation Pattern Of Houses," Economic Inquiry, Western Economic Association International, vol. 55(1), pages 580-600, January.
    11. Lorraine Ivancic & Kevin J. Fox, 2013. "Understanding Price Variation Across Stores and Supermarket Chains: Some Implications for CPI Aggregation Methods," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 59(4), pages 629-647, December.
    12. Ryan Greenaway-McGrevy & Kade Sorensen, 2021. "A spatial model averaging approach to measuring house prices," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-32, December.
    13. Diewert, Erwin, 2007. "The Paris OECD-IMF Workshop on Real Estate Price Indexes: Conclusions and Future Directions," Economics working papers diewert-07-01-03-08-12-12, Vancouver School of Economics, revised 31 Jan 2007.
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    15. Raquel Arévalo Tomé & José María Chamorro Rivas, "undated". "Geographic Heterogeneity in Housing. Evidence from Spain," Studies on the Spanish Economy 203, FEDEA.
    16. 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.
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