We develop a vector autoregressive framework for combining the information in an external instrument with the information in the second moments of the data to identify latent monetary shocks in the United States. We show that the framework improves the identification of the structural model and...

We develop a vector autoregressive framework for combining the information in an external instrument with the information in the second moments of the data to identify latent monetary shocks in the United States. We show that the framework improves the identification of the structural model and...

This paper introduces changes in the level of ambiguity as a complementary source of time-varying risk aversion. We show in a consumption-based asset pricing model with simultaneously risky and ambiguous assets that a rise in the level of ambiguity raises investors' risk aversion. The effect is...

We develop a vector autoregressive framework that combines an external instrument and heteroskedasticity for the identification of monetary policy shocks. We show that exploiting both types of information sharpens structural inference, allows testing both the relevance and exogeneity condition...

This paper introduces changes in the level of ambiguity as a complementary source of time-varying risk aversion. We show in a consumption-based asset pricing model with simultaneously risky and ambiguous assets that a rise in the level of ambiguity raises investors' risk aversion. The effect is...

We develop a structural vector autoregressive framework that combines external instruments and heteroskedasticity for identification of monetary policy shocks. We show that exploiting both types of information sharpens structural inference, allows testing the relevance and exogeneity condition...

We develop a structural vector autoregressive framework that combines external instruments and heteroskedasticity for identification of monetary policy shocks. We show that exploiting both types of information sharpens structural inference, allows testing the relevance and exogeneity condition...

This paper proposes a Bayesian approach to assess if the data support candidate set-identifying restrictions for Vector Autoregressive models. The researcher is uncertain about the validity of some sign restrictions that she is contemplating to use. She therefore expresses her uncertainty with a...