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Peer Groups and Bias Detection in Least Squares Regression

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  • Blankmeyer, Eric

Abstract

A correlation between regressors and disturbances presents challenging problems in linear regression. In the context of spatial econometrics LeSage and Pace (2009) show that an autoregressive model estimated by maximum likelihood may be able to detect least squares bias. I suggest that spatial neighbors can be replaced by “peer groups” as in Blankmeyer et al. (2011), thereby extending considerably the range of contexts where the autoregressive model can be utilized. The procedure is applied to two data sets and in a simulation

Suggested Citation

  • Blankmeyer, Eric, 2021. "Peer Groups and Bias Detection in Least Squares Regression," MPRA Paper 110866, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:110866
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    File URL: https://mpra.ub.uni-muenchen.de/110866/1/MPRA_paper_110866.pdf
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    References listed on IDEAS

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    1. Gordon B. Dahl & Katrine V. L?ken & Magne Mogstad, 2014. "Peer Effects in Program Participation," American Economic Review, American Economic Association, vol. 104(7), pages 2049-2074, July.
    2. Adriaan R. Soetevent, 2006. "Empirics of the Identification of Social Interactions; An Evaluation of the Approaches and Their Results," Journal of Economic Surveys, Wiley Blackwell, vol. 20(2), pages 193-228, April.
    3. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    4. Conley, Timothy G. & Topa, Giorgio, 2007. "Estimating dynamic local interactions models," Journal of Econometrics, Elsevier, vol. 140(1), pages 282-303, September.
    5. James P. LeSage & R. Kelley Pace, 2014. "The Biggest Myth in Spatial Econometrics," Econometrics, MDPI, vol. 2(4), pages 1-33, December.
    6. Brueckner, Jan K., 1998. "Testing for Strategic Interaction Among Local Governments: The Case of Growth Controls," Journal of Urban Economics, Elsevier, vol. 44(3), pages 438-467, November.
    7. Case, Anne C. & Rosen, Harvey S. & Hines, James Jr., 1993. "Budget spillovers and fiscal policy interdependence : Evidence from the states," Journal of Public Economics, Elsevier, vol. 52(3), pages 285-307, October.
    8. Edward L. Glaeser & Bruce Sacerdote & José A. Scheinkman, 1996. "Crime and Social Interactions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(2), pages 507-548.
    9. Giacomo De Giorgi & Michele Pellizzari & Silvia Redaelli, 2010. "Identification of Social Interactions through Partially Overlapping Peer Groups," American Economic Journal: Applied Economics, American Economic Association, vol. 2(2), pages 241-275, April.
    10. Durlauf, Steven N., 1994. "Spillovers, stratification, and inequality," European Economic Review, Elsevier, vol. 38(3-4), pages 836-845, April.
    11. Stephanie von Hinke & George Leckie & Cheti Nicoletti, 2019. "The Use of Instrumental Variables in Peer Effects Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 1179-1191, October.
    12. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    13. Bruce Sacerdote, 2001. "Peer Effects with Random Assignment: Results for Dartmouth Roommates," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(2), pages 681-704.
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    More about this item

    Keywords

    peer groups; least-squares bias; spatial autoregression;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

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