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Estimating an inflation index by quantile regression

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  • Eric Blankmeyer

Abstract

This article gives a methodology for estimating an inflation index using the quantile regression of Bassett and Koenker. The regression -- orthogonal in the logarithmic price changes -- is computed by linear programming for each percentile of inflation; and the results are bootstrapped to estimate standard errors. The procedure is applied to monthly data on seven metals.

Suggested Citation

  • Eric Blankmeyer, 2012. "Estimating an inflation index by quantile regression," Applied Economics Letters, Taylor & Francis Journals, vol. 19(2), pages 185-187, February.
    • Handle: RePEc:taf:apeclt:v:19:y:2012:i:2:p:185-187
    DOI: 10.1080/13504851.2011.570706
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    References listed on IDEAS

    as
    1. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    2. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, January.
    3. Victor Chernozhukov, 2005. "Extremal quantile regression," Papers math/0505639, arXiv.org.
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    Cited by:

    1. Kenneth W. Clements & H. Y. Izan & Yihui Lan, 2013. "Volatility and stock price indexes," Applied Economics, Taylor & Francis Journals, vol. 45(22), pages 3255-3262, August.

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