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Variance Estimation in the Swedish Consumer Price Index

Author

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  • Dalen, Jurgen
  • Ohlsson, Esbjorn

Abstract

In large parts of the Swedish consumer price index, products and outlets are sampled independently, yielding a two-dimensional, cross-classified sample. In this paper, a design-based variance formula is derived by exploiting the general theory for cross-classified sampling. Numerical variance estimates are given, along with a description of some of the detailed procedures involved in their computation. Finally, the implications of these estimates on the actual allocation are discussed.

Suggested Citation

  • Dalen, Jurgen & Ohlsson, Esbjorn, 1995. "Variance Estimation in the Swedish Consumer Price Index," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 347-356, July.
    • Handle: RePEc:bes:jnlbes:v:13:y:1995:i:3:p:347-56
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    Cited by:

    1. Hélène Juillard & Guillaume Chauvet & Anne Ruiz-Gazen, 2017. "Estimation Under Cross-Classified Sampling With Application to a Childhood Survey," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 850-858, April.
    2. Mick Silver, 2016. "How to Better Measure Hedonic Residential Property Price Indexes," IMF Working Papers 2016/213, International Monetary Fund.
    3. Skinner, C.J., 2015. "Cross-classified sampling: Some estimation theory," Statistics & Probability Letters, Elsevier, vol. 104(C), pages 163-168.
    4. Skinner, C. J., 2015. "Cross-classified sampling: some estimation theory," LSE Research Online Documents on Economics 62261, London School of Economics and Political Science, LSE Library.
    5. Paul A. Smith, 2021. "Estimating Sampling Errors in Consumer Price Indices," International Statistical Review, International Statistical Institute, vol. 89(3), pages 481-504, December.
    6. Carlo De Gregorio, 2012. "Sample size for the estimate of consumer price subindices with alternative statistical designs," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 14(1), pages 19-47, October.

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