A generalization of Jeffreys' rule for non regular models

Abstract

We propose a generalization of the one-dimensional Jeffreys’ rule in order to obtain non informative prior distributions for non regular mod- els, taking into account the comments made by Jeffreys in his arti- cle of 1946. These non informatives are parameterization invariant and the Bayesian intervals have good behavior in frequentist inference. In some important cases, we can generate non informative distributions for multi-parameter models with non regular parameters. In non regular models, the Bayesian method offers a satisfactory solution to the infer- ence problem and also avoids the problem that the maximum likelihood estimator has with these models. Finally, we obtain non informative dis- tributions in job-search and deterministic frontier production homoge- nous models