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Imputation and Price Indexes: Theory and Evidence from the International Price Program

Author

Listed:
  • Robert Feenstra
  • Erwin Diewert

    (Department of Economics, University of California Davis)

Abstract

The goal of this paper is to theoretically and empirically demonstrate the consequences of different imputation methods, using recent data from the International Price Program. We suppose that prices are missing due to random or erratic reporting. We consider three different imputation methods: carry-forward, which just assumes that the missing price is the same as in the previous period; cell-mean, which imputes the missing price using either the short-term or long-term index for related commodities; and linear interpolation, which uses the last and next observations for the item to linearly interpolate. Certain hybrid techniques, combining either carry-forward or cell-mean with linear interpolation, are also considered. Our conclusions are: (1) Some imputation is better than no imputation; (2) the short term cell-mean introduces some â??noiseâ?? into the price index: (3) linear interpolation results in less fluctuation of prices than the true series: (4) combining either carry-forward or cell-mean with linear interpolation gives similar results.

Suggested Citation

  • Robert Feenstra & Erwin Diewert, 2003. "Imputation and Price Indexes: Theory and Evidence from the International Price Program," Working Papers 012, University of California, Davis, Department of Economics.
  • Handle: RePEc:cda:wpaper:00-12
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    File URL: http://wp.econ.ucdavis.edu/00-12.pdf
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    References listed on IDEAS

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    1. Mark A. Wynne, 2008. "Core inflation: a review of some conceptual issues," Review, Federal Reserve Bank of St. Louis, issue May, pages 205-228.
    2. Michael F. Bryan & Stephen G. Cecchetti, 1993. "The consumer price index as a measure of inflation," Economic Review, Federal Reserve Bank of Cleveland, issue Q IV, pages 15-24.
    3. Diewert, W. Erwin, 1998. "High Inflation, Seasonal Commodities, And Annual Index Numbers," Macroeconomic Dynamics, Cambridge University Press, vol. 2(04), pages 456-471, 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. Stephen G. Cecchetti, 1997. "Measuring short-run inflation for central bankers," Review, Federal Reserve Bank of St. Louis, issue May, pages 143-155.
    6. repec:cup:macdyn:v:2:y:1998:i:4:p:456-71 is not listed on IDEAS
    7. Michael F. Bryan & Stephen G. Cecchetti, 1994. "Measuring Core Inflation," NBER Chapters,in: Monetary Policy, pages 195-219 National Bureau of Economic Research, Inc.
    8. repec:cup:macdyn:v:3:y:1999:i:1:p:48-68 is not listed on IDEAS
    9. Clements, Kenneth W & Izan, H Y, 1987. "The Measurement of Inflation: A Stochastic Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(3), pages 339-350, July.
    10. Diewert, W. Erwin, 1999. "Index Number Approaches To Seasonal Adjustment," Macroeconomic Dynamics, Cambridge University Press, vol. 3(01), pages 48-68, March.
    11. Paul A. Armknecht & Fenella Maitland-Smith, 1999. "Price Imputation and Other Techniques for Dealing with Missing Observations, Seasonality and Quality Change in Price Indices," IMF Working Papers 99/78, International Monetary Fund.
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    Cited by:

    1. Emi Nakamura & Jón Steinsson, 2012. "Lost in Transit: Product Replacement Bias and Pricing to Market," American Economic Review, American Economic Association, vol. 102(7), pages 3277-3316, December.

    More about this item

    Keywords

    imputation; price index; interpolation;

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

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