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Working Paper
Inference in Bayesian Proxy-SVARs
Motivated by the increasing use of external instruments to identify structural vector autoregressions SVARs), we develop algorithms for exact finite sample inference in this class of time series models, commonly known as proxy SVARs. Our algorithms make independent draws from the normal-generalized-normal family of conjugate posterior distributions over the structural parameterization of a proxy-SVAR. Importantly, our techniques can handle the case of set identification and hence they can be used to relax the additional exclusion restrictions unrelated to the external instruments often ...
Working Paper
Asymptotically Valid Bootstrap Inference for Proxy SVARs
Proxy structural vector autoregressions identify structural shocks in vector autoregressions with external variables that are correlated with the structural shocks of interest but uncorrelated with all other structural shocks. We provide asymptotic theory for this identification approach under mild ?-mixing conditions that cover a large class of uncorrelated, but possibly dependent innovation processes, including conditional heteroskedasticity. We prove consistency of a residual-based moving block bootstrap for inference on statistics such as impulse response functions and forecast error ...
Working Paper
From Bank Lending Standards to Bank Credit Conditions: An SVAR Approach
This paper uses a structural vector autoregressive (SVAR) model—identified with an external monetary policy instrument and sign restrictions—to derive a measure of bank credit conditions from changes in bank lending standards. The model incorporates data on interest rates, bank credit, and survey-based measures of bank lending standards to identify monetary policy, credit demand, and credit supply shocks. Using these identified shocks, we construct a novel measure of bank credit conditions that corresponds to the component of credit growth that would occur if credit demand remained ...