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We propose identification robust statistics for testing hypotheses on therisk premia in dynamic affine term structure models. We do so using the momentequation specification proposed for these models in Adrian et al. (2013). Weextend the subset (factor) Anderson-Rubin test from Guggenberger et al. (2012)to models with multiple dynamic factors and time-varying risk prices. Unlikeprojection-based tests, it provides a computationally tractable manner toconduct identification robust tests on a larger number of parameters. Weanalyze 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 withoutfull-rank beta's or with sampling errors of comparable size as the loadings.Empirical applications show that some factors, though potentially weak, maydrive the time variation of risk prices, and weak identification issues aremore prominent in multi-factor models.

Identification Robust Inference for the Risk Premium in Term Structure Models