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Extreme Bounds Analysis

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Abstract

Extreme bounds analysis is a global sensitivity analysis that applies to the choice of variables in a linear regression. Rather than a discrete search over models that include or exclude subsets of the variables, this sensitivity analysis answers the question: how extreme can the estimates be if any linear homogenous restrictions on a selected subset of the coefficients are allowed? When these bounds are too wide to be useful, narrower bounds can be found by restricting the set of prior distributions that underlie the sensitivity analysis.

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Bibliography

  • Leamer, E. 1978. Specification searches: Ad Hoc Inference with Non experimental data. New York: John Wiley and Sons.

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  • Leamer, E. 1981. Sets of estimates of location. Econometrica 49: 193–204.

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  • Leamer, E. 1982. Sets of posterior means with bounded variance priors. Econometrica 50: 725–736.

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  • Leamer, E., and G. Chamberlain. 1976. Matrix weighted averages and posterior bounds. Journal of the Royal Statistical Society, Series B 38: 73–84.

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© 2018 Macmillan Publishers Ltd.

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Leamer, E.E. (2018). Extreme Bounds Analysis. In: The New Palgrave Dictionary of Economics. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-349-95189-5_2167

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