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A Practical Review of Methods to Estimate Overcharges using Linear Regression

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  • Inderst, Roman
  • Milde, Christopher

Abstract

Arguably the most widely used techniques for estimating price overcharges from competition law infringements are the dummy variable and the forecasting approaches using linear regression analysis. While rarely used in practice, in this note we make use of the fully interacted dummy variable approach to review some basic properties of all three approaches. We show under which conditions and for which estimands of interest these approaches are equivalent and when they differ. We also note some interesting additional choices an interaction approach allows.

Suggested Citation

  • Inderst, Roman & Milde, Christopher, 2019. "A Practical Review of Methods to Estimate Overcharges using Linear Regression," EconStor Preprints 253660, ZBW - Leibniz Information Centre for Economics.
  • Handle: RePEc:zbw:esprep:253660
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    References listed on IDEAS

    as
    1. White, Halbert, 2006. "Time-series estimation of the effects of natural experiments," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 527-566.
    2. Salkever, David S., 1976. "The use of dummy variables to compute predictions, prediction errors, and confidence intervals," Journal of Econometrics, Elsevier, vol. 4(4), pages 393-397, November.
    3. Higgins, Richard S. & Johnson, Paul A., 2003. "The mean effect of structural change on the dependent variable is accurately measured by the intercept change alone," Economics Letters, Elsevier, vol. 80(2), pages 255-259, August.
    4. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    JEL classification:

    • K13 - Law and Economics - - Basic Areas of Law - - - Tort Law and Product Liability; Forensic Economics
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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