  This paper presents a set of tests and an algorithm for agnostic, data-driven
selection among macroeconomic DSGE models inspired by structure learning
methods for DAGs. As the log-linear state-space solution to any DSGE model is
also a DAG it is possible to use associated concepts to identify a unique
ground-truth state-space model which is compatible with an underlying DGP,
based on the conditional independence relationships which are present in that
DGP. In order to operationalise search for this ground-truth model, the
algorithm tests feasible analogues of these conditional independence criteria
against the set of combinatorially possible state-space models over observed
variables. This process is consistent in large samples. In small samples the
result may not be unique, so conditional independence tests can be combined
with likelihood maximisation in order to select a single optimal model. The
efficacy of this algorithm is demonstrated for simulated data, and results for
real data are also provided and discussed.
