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Strategic behavior in regressions: an experimental

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  • Javier Perote
  • Juan Perote-Peña
  • Marc Vorsatz

Abstract

We study experimentally in the laboratory the situation when individuals have to report their private information (that is commonly known to be the sum of an observable and a random component) to a public authority that then makes inference about the true value hold by each of the individuals. It is assumed that individuals prefer this inferred or predicted value to be as close as possible to the their true value. Consistent with the theoretical literature, we show that the participants in our experiment misrepresent their private information more under the OLS than under the resistant line estimator (which extends the median voter theorem to the two{dimensional setting). Moreover, only the resistant line estimator is empirically unbiased and subjects earn significantly less if the OLS estimator is applied.

Suggested Citation

  • Javier Perote & Juan Perote-Peña & Marc Vorsatz, 2012. "Strategic behavior in regressions: an experimental," Working Papers 2012-07, FEDEA.
    • Handle: RePEc:fda:fdaddt:2012-07
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    References listed on IDEAS

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    1. Perote, Javier & Perote-Pena, Juan, 2004. "Strategy-proof estimators for simple regression," Mathematical Social Sciences, Elsevier, vol. 47(2), pages 153-176, March.
    2. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
    3. ,, 2009. "Strategy-proofness and single-crossing," Theoretical Economics, Econometric Society, vol. 4(2), June.
    4. Sprumont, Yves, 1991. "The Division Problem with Single-Peaked Preferences: A Characterization of the Uniform Allocation Rule," Econometrica, Econometric Society, vol. 59(2), pages 509-519, March.
    5. Edward P. Lazear, 2000. "Performance Pay and Productivity," American Economic Review, American Economic Association, vol. 90(5), pages 1346-1361, December.
    6. H. Moulin, 1980. "On strategy-proofness and single peakedness," Public Choice, Springer, vol. 35(4), pages 437-455, January.
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    1. Nadezhda Gribkova & Ričardas Zitikis, 2018. "A User-Friendly Algorithm for Detecting the Influence of Background Risks on a Model," Risks, MDPI, vol. 6(3), pages 1-11, September.

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