The study shows that using improper priors for model parameters doesn't always lead to undefined Bayes factors, contrary to what was previously believed. By introducing new methods, researchers found ways to make Bayes factors well-defined even with improper priors. This expands the types of priors that can be used for model comparison, including the commonly used shrinkage prior. The study demonstrates these findings through a simple cointegration analysis.