Comparing Efficiency Across Markets: An Extension and Critique of the Zhang and Bartels (1998) Methodology

Ruben Chumpitaz, Kristiaan Kerstens, Nicholas Paparoidamis and Matthias Staat
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Ruben Chumpitaz: IESEG School of Management, LEM-CNRS (UMR 8179)
Matthias Staat: University of Mannheim

No 2010-ECO-01, Working Papers from IESEG School of Management

Abstract: The use of non-parametric frontier methods for the evaluation of product market efficiency in heterogeneous markets seems to have gained some popularity recently. However, the statistical properties of these frontier estimators have been largely ignored. The main point is that nonparametric frontier estimators are biased and that the degree of bias depends on specific sample properties, most importantly sample size and number of dimensions of the model. To investigate the effect of this bias on comparing market efficiency, this contribution estimates the efficiency for several datasets for two main product categories. Following Zhang and Bartels (1998), these results comprise re-estimates for the larger samples limiting their size to that of the smaller samples when the model dimensions for different samples are identical. Furthermore, sample sizes are adjusted to mitigate the eventual differences in dimensions in specification. This allows comparing market efficiency for different markets on a more equal footing, since it reduces the bias effect to a minimum making the comparison of market efficiency possible. However, the article also points out the critical limitations of this Zhang and Bartels (1998) approach in certain respects. Apart from reporting these negative results, we also offer some suggestions for future work.

Keywords: Market Efficiency; Heterogeneous Product Markets; Bias; Monte-Carlo Simulation (search for similar items in EconPapers)
Pages: 24 pages
Date: 2010-01
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Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Comparing efficiency across markets: an extension and critique of the Zhang and Bartels (1998) methodology (2010)
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