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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.

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File URL: http://www.sfu.ca/econ-research/RePEc/sfu/sfudps/dp11-02.pdf
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Bibliographic Info

Paper provided by Department of Economics, Simon Fraser University in its series Discussion Papers with number dp11-02.

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Length: 43
Date of creation: Aug 2011
Date of revision:
Handle: RePEc:sfu:sfudps:dp11-02

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Postal: Department of Economics, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
Phone: (778)782-3508
Fax: (778)782-5944
Web page: http://www.sfu.ca/economics.html
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Postal: Working Paper Coordinator, Department of Economics, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
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Web: http://www.sfu.ca/economics/research/publications.html

Related research

Keywords: sensitivity analysis; partial identification; endogeneity;

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References

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  1. Jörg Stoye, 2008. "More on confidence intervals for partially identified parameters," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP11/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  2. Arthur Lewbel, 2010. "Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 30(1), pages 67-80, December.
  3. Aviv Nevo & Adam Rosen, 2008. "Identification with imperfect instruments," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies CWP16/08, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. 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, University of Wisconsin Press, vol. 37(3), pages 510-539.
  5. 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, University of Chicago Press, vol. 113(1), pages 151-184, February.
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Cited by:
  1. Dujardin, Claire & Goffette-Nagot, Florence, 2010. "Neighborhood effects on unemployment?: A test à la Altonji," Regional Science and Urban Economics, Elsevier, Elsevier, vol. 40(6), pages 380-396, November.

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