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Structural Determinants of Cumulative Endogeneity Bias

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  • David Mayston

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 interrelationships 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

  • David Mayston, "undated". "Structural Determinants of Cumulative Endogeneity Bias," Discussion Papers 05/11, Department of Economics, University of York.
  • Handle: RePEc:yor:yorken:05/11
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    File URL: https://www.york.ac.uk/media/economics/documents/discussionpapers/2005/0511.pdf
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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. Alan B. Krueger, 1999. "Experimental Estimates of Education Production Functions," The Quarterly Journal of Economics, Oxford University Press, vol. 114(2), pages 497-532.
    3. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
    4. Hanushek, Eric A., 2006. "School Resources," Handbook of the Economics of Education, Elsevier.
    5. 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.
    6. 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.
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    Cited by:

    1. Eva Liljeblom & Sabur Mollah & Patrik Rotter, 2015. "Do dividends signal future earnings in the Nordic stock markets?," Review of Quantitative Finance and Accounting, Springer, vol. 44(3), pages 493-511, April.
    2. Shengyi Gao & Patricia Mokhtarian & Robert Johnston, 2008. "Exploring the connections among job accessibility, employment, income, and auto ownership using structural equation modeling," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(2), pages 341-356, June.

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    Keywords

    Multivariate regression analysis; Cumulative endogeneity bias; Evidence-based policy;

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