eba is used to perform extreme bounds analysis (EBA), a global sensitivity test that examines the robustness of the association between a dependent variable and a variety of possible determinants. The eba function performs a demanding version of EBA, proposed by Leamer (1985), that focuses on the upper and lower extreme bounds of regression estimates, as well as a more flexible version proposed by Sala-i-Martin (1997). Sala-i-Martin's EBA considers the entire distribution of regression coefficients. For Sala-i-Martin's version of extreme bounds analysis, eba estimates results for both the normal model (in which regression coefficients are assumed to be normally distributed across models) and the generic model (where no such assumption is made).
The package ExtremeBounds performs extreme bounds analysis (EBA), a global sensitivity test that examines the robustness of the association between a dependent variable and a variety of possible determinants. It supports a demanding version of EBA, proposed by Leamer (1985), that focuses on the upper and lower extreme bounds of regression estimates, as well as a more flexible version proposed by Sala-i-Martin (1997). Sala-i-Martin's EBA considers the entire distribution of regression coefficients. For Sala-i-Martin's version of extreme bounds analysis, the package ExtremeBounds estimates results for both the normal model (in which regression coefficients are assumed to be normally distributed across models) and the generic model (where no such assumption is made).
hist.eba is used to generate a set of histograms that present the results of extreme bounds analysis graphically. Each histogram illustrates the distribution of regression coefficients across the models estimated in the course of EBA. In addition, function hist.eba can overlay each histogram with lines that indicate the value of the regression coefficient assumed under the null hypothesis (argument mu.show), as well as with curves that indicate the distribution's kernel density (argument density.show) and a normally distributed approximation (argument normal.show). Additional formatting options are available.
hist.eba prints the results of extreme bounds analysis (EBA; performed by the eba function) and returns the printed object invisibly (via invisible(x)). The function prints out information about the distribution and significance of estimated regression coefficients, the results of Leamer's EBA, as well as those of Sala-i-Martin's EBA (both the normal and generic model).
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