In a recent paper on Federal Reserve inflation forecast errors ( summary blog post , paper ) I wanted a way to easily compare the coefficients for a set of covariates (a) estimated from different types of parametric models using (b) matched and non-matched data. I guess the most basic way to do this would be to have a table of columns showing point estimates and confidence intervals from each estimation model. But making meaningful comparisons with this type of table would be tedious. What I ended up doing was creating a kind of stacked caterpillar plot . Here it is: Comparing 95% Confidence Intervals I think this plot lets you clearly and quickly compare the confidence intervals estimated from the different models. I didn't include the coefficient point estimates because I was most interested in comparing the ranges. The dots added too much clutter. I have a link to the full replication code at the end of the post, but these are the basic steps:...