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Strategy-proof estimators for simple regression

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  • Perote, Javier
  • Perote-Pena, Juan

Abstract

In this paper we propose a whole class of estimators (“clockwise repeated median estimators” or CRM) for the simple regression model that are immune to manipulation by the agents generating the data. Although strategic considerations affecting the stability of the estimated parameters in regression models have already been studied (the Lucas critique), few efforts have been made to design estimators that are incentive compatible. We find that some well-known robust estimators proposed in the literature like the resistant line method are included in our family. Finally, we also undertake a Monte Carlo study to compare the distribution of some estimators that are robust to data manipulation with the OLS estimators under some specific data manipulation process.
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Suggested Citation

  • Perote, Javier & Perote-Pena, Juan, 2004. "Strategy-proof estimators for simple regression," Mathematical Social Sciences, Elsevier, vol. 47(2), pages 153-176, March.
    • Handle: RePEc:eee:matsoc:v:47:y:2004:i:2:p:153-176
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    References listed on IDEAS

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    1. H. Moulin, 1980. "On strategy-proofness and single peakedness," Public Choice, Springer, vol. 35(4), pages 437-455, January.
    2. Salvador Barbera & Matthew Jackson, 1991. "A Characterization of Strategy-Proof Social Choice Functions for Economies with Pure Public Goods," Discussion Papers 964, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    3. Gibbard, Allan, 1973. "Manipulation of Voting Schemes: A General Result," Econometrica, Econometric Society, vol. 41(4), pages 587-601, July.
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    Cited by:

    1. Eliaz, Kfir & Spiegler, Ran, 2022. "On incentive-compatible estimators," Games and Economic Behavior, Elsevier, vol. 132(C), pages 204-220.
    2. Nadezhda Gribkova & Ričardas Zitikis, 2019. "Statistical detection and classification of background risks affecting inputs and outputs," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 1-18, April.
    3. Javier Perote & Juan Perote-Peña & Marc Vorsatz, 2012. "Strategic behavior in regressions: an experimental," Working Papers 2012-07, FEDEA.
    4. 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.
    5. Mehmet Caner & Kfir Eliaz, 2021. "Shoiuld Humans Lie to Machines: The Incentive Compatibility of Lasso and General Weighted Lasso," Papers 2101.01144, arXiv.org, revised Sep 2021.
    6. Javier Perote & Juan Perote-Peña & Marc Vorsatz, 2015. "Strategic behavior in regressions: an experimental study," Theory and Decision, Springer, vol. 79(3), pages 517-546, November.

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    More about this item

    JEL classification:

    • D78 - Microeconomics - - Analysis of Collective Decision-Making - - - Positive Analysis of Policy Formulation and Implementation
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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