  We propose identification robust statistics for testing hypotheses on the
risk premia in dynamic affine term structure models. We do so using the moment
equation specification proposed for these models in Adrian et al. (2013). We
extend the subset (factor) Anderson-Rubin test from Guggenberger et al. (2012)
to models with multiple dynamic factors and time-varying risk prices. Unlike
projection-based tests, it provides a computationally tractable manner to
conduct identification robust tests on a larger number of parameters. We
analyze the potential identification issues arising in empirical studies.
Statistical inference based on the three-stage estimator from Adrian et al.
(2013) requires knowledge of the factors' quality and is misleading without
full-rank beta's or with sampling errors of comparable size as the loadings.
Empirical applications show that some factors, though potentially weak, may
drive the time variation of risk prices, and weak identification issues are
more prominent in multi-factor models.
