Journal Article

Bayesian Analysis of Static and Dynamic Factor Models: An Ex-Post Approach towards the Rotation Problem

Journal of Econometrics

Due to their indeterminacies, static and dynamic factor models require identifying assumptions

to guarantee uniqueness of the parameter estimator. The indeterminacy of the parameter

estimator with respect to an orthogonal transformation is known as the rotation problem. The

typical strategy in Bayesian factor analysis to solve the rotation problem is to introduce ex-ante

constraints on certain model parameters via degenerate and truncated prior distributions. This

strategy, however, results in posterior distributions whose shapes depend on the ordering of the

variables in the data set. We propose an alternative approach where the rotation problem is

solved ex-post using Procrustean postprocessing. The resulting order invariance of the posterior

estimator is illustrated in a simulation study and an empirical application using an established

data set containing 120 macroeconomic time series. Favorable properties of the ex-post approach

with respect to convergence, statistical and numerical accuracy are revealed.

Authors

Christian Aßmann
Markus Pape