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Bounding a linear causal effect using relative correlation restrictions

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Abstract

This paper describes and implements a simple approach to the most common problem in applied microeconometrics: estimating a linear causal effct when the explanatory variable of interest might be correlated with relevant unobserved variables. The main idea is to place restrictions on the correlation between the variable of interest and relevant unobserved variables relative to the correlation between the variable of interest and observed control variables. These relative correlation restrictions allow a researcher to construct informative bounds on parameter estimates, and to assess the sensitivity of conventional estimates to plausible deviations from the identifying assumptions. The estimation method and its properties are described, and two empirical applications are demonstrated.

Suggested Citation

  • Brian Krauth, 2011. "Bounding a linear causal effect using relative correlation restrictions," Discussion Papers dp11-02, Department of Economics, Simon Fraser University.
  • Handle: RePEc:sfu:sfudps:dp11-02
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    File URL: http://www.sfu.ca/econ-research/RePEc/sfu/sfudps/dp11-02.pdf
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    1. repec:taf:jnlbes:v:30:y:2012:i:1:p:67-80 is not listed on IDEAS
    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.
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    4. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 151-184, February.
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    6. Jennifer M. Mellor & Jeffrey Milyo, 2002. "Income Inequality and Health Status in the United States: Evidence from the Current Population Survey," Journal of Human Resources, University of Wisconsin Press, vol. 37(3), pages 510-539.
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    8. Jorg Stoye, 2009. "More on Confidence Intervals for Partially Identified Parameters," Econometrica, Econometric Society, vol. 77(4), pages 1299-1315, July.
    9. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "An Evaluation of Instrumental Variable Strategies for Estimating the Effects of Catholic Schooling," Journal of Human Resources, University of Wisconsin Press, vol. 40(4), pages 791-821.
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    Cited by:

    1. E. Somanathan & Randall Bluffstone, 2015. "Biogas: Clean Energy Access with Low-Cost Mitigation of Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 265-277, October.
    2. Dujardin, Claire & Goffette-Nagot, Florence, 2010. "Neighborhood effects on unemployment?: A test à la Altonji," Regional Science and Urban Economics, Elsevier, vol. 40(6), pages 380-396, November.
    3. Halkos, George E. & Paizanos, Epameinondas Α., 2013. "The effect of government expenditure on the environment:An empirical investigation," Ecological Economics, Elsevier, vol. 91(C), pages 48-56.
    4. Amin, Vikesh & Flores, Carlos A. & Flores-Lagunes, Alfonso & Parisian, Daniel J., 2016. "The effect of degree attainment on arrests: Evidence from a randomized social experiment," Economics of Education Review, Elsevier, vol. 54(C), pages 259-273.
    5. Halkos, George & Paizanos, Epameinondas, 2014. "Exploring the effect of economic growth and government expenditure on the environment," MPRA Paper 56084, University Library of Munich, Germany.
    6. Ransom, Michael R. & Ransom, Tyler, 2017. "Do High School Sports Build or Reveal Character?," IZA Discussion Papers 11110, Institute for the Study of Labor (IZA).

    More about this item

    Keywords

    sensitivity analysis; partial identification; endogeneity;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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