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.
Bayesian econometrics Extreme bounds analysis Heteroscedasticity Nonparametric models and methods Probability White-corrected standard errors
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Leamer, E. 1978. Specification searches: Ad Hoc Inference with Non experimental data. New York: John Wiley and Sons.Google Scholar