Econometric issues that are considered fundamental in the development of Bayesian structural
inference within a Simultaneous Equation Model are surveyed. The difficulty of specifying
prior information which is of interest to economists and which yields tractable posterior
and predictive distributions has started this line of research. A major issue is the
nonstandard shape of the likelihood due to reduced rank restrictions. It implies that
existence of structural posterior moments under vague prior information is a nontrivial
issue. The problem is illustrated through simple examples using artificially generated data
in a so-called limited information framework where the connection with the problem of weak
instruments in classical econometrics is also described. A positive development is
Bayesian inference of implied characteristics, in particular, dynamic features of a
Simultaneous Equation Model. The potential of Bayesian structural inference, using a
predictive approach for prior specification and using Monte Carlo simulation techniques
for computational purposes, is illustrated by means of a prior and posterior analysis of
the US business cycle in the period of the depression. A structural prior is elicited
through investigation of the implied predictive features