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The determinants of cumulative endogeneity bias in multivariate analysis

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  • Mayston, David

Abstract

The BLU properties of OLS estimators under known assumptions have encouraged the widespread use of OLS multivariate regression analysis in many empirical studies that are based upon a conceptual model of a single explanatory equation. However, such a model may well be an imperfect empirical approximation to the valid underlying conceptual model, that may well contain several important additional inter-relationships between the relevant variables. In this paper, we examine the conditions under which we can predict the direction of the resultant endogeneity bias that will prevail in the OLS asymptotic parameter estimates for any given endogenous or predetermined variable, and the extent to which we can rely upon simple heuristics in this process. We also identify the underlying structural parameters to which the magnitude of the endogeneity bias is sensitive. The importance of such sensitivity analysis has been underlined by an increasing awareness of the inability of standard diagnostic tests to shed light upon the extent of the endogeneity bias, rather than upon merely its existence. The paper examines the implications of the analysis for statistical inferences about the true value of the regression coefficients and the validity of associated t-statistics.

Suggested Citation

  • Mayston, David, 2009. "The determinants of cumulative endogeneity bias in multivariate analysis," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1120-1136, July.
  • Handle: RePEc:eee:jmvana:v:100:y:2009:i:6:p:1120-1136
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    References listed on IDEAS

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    1. Wu, De-Min, 1973. "Alternative Tests of Independence Between Stochastic Regressors and Disturbances," Econometrica, Econometric Society, vol. 41(4), pages 733-750, July.
    2. Jan R. Magnus & Andrey L. Vasnev, 2007. "Local sensitivity and diagnostic tests," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 166-192, March.
    3. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    4. Nakamura, Alice & Nakamura, Masao, 1998. "Model specification and endogeneity," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 213-237.
    5. Nakamura, Alice & Nakamura, Masao, 1985. "On the performance of tests by Wu and by Hausman for detecting the ordinary least squares bias problem," Journal of Econometrics, Elsevier, vol. 29(3), pages 213-227, September.
    6. Hanushek, Eric A, 1986. "The Economics of Schooling: Production and Efficiency in Public Schools," Journal of Economic Literature, American Economic Association, vol. 24(3), pages 1141-1177, September.
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    Cited by:

    1. repec:spr:annopr:v:250:y:2017:i:1:d:10.1007_s10479-015-2074-3 is not listed on IDEAS
    2. Philippe Gorry & Diego Useche, 2017. "Orphan Drug Designations as Valuable Intangible Assets for IPO Investors in Pharma-Biotech Companies," NBER Chapters,in: Economic Dimensions of Personalized and Precision Medicine National Bureau of Economic Research, Inc.
    3. David Mayston, 2015. "Analysing the effectiveness of public service producers with endogenous resourcing," Journal of Productivity Analysis, Springer, vol. 44(1), pages 115-126, August.
    4. Annamaria Conti & Jerry Thursby & Marie Thursby, 2013. "Patents as Signals for Startup Financing," Journal of Industrial Economics, Wiley Blackwell, vol. 61(3), pages 592-622, September.
    5. Useche, Diego, 2014. "Are patents signals for the IPO market? An EU–US comparison for the software industry," Research Policy, Elsevier, vol. 43(8), pages 1299-1311.
    6. David J. Mayston, 2015. "Data envelopment analysis, endogeneity and the quality frontier for public services," Discussion Papers 15/05, Department of Economics, University of York.

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