Search

In this paper we discuss general identification results for Structural Vector Autoregressions (SVARs) with external instruments, considering the case in which r valid instruments are used to identify g Ï 1 structural shocks, where r Ï g. We endow the SVAR with an auxiliary statistical model for the external instruments which is a system of reduced form equations. The SVAR and the auxiliary model for the external instruments jointly form a "larger" SVAR characterized by a particularly restricted parametric structure, and are connected by the covariance matrix of their disturbances which incorporates the "relevance" and "exogeneity" conditions. We discuss identification results and likelihood-based estimation methods both in the "multiple shocks" approach, where all structural shocks are of interest, and in the "partial shock" approach, where only a subset of the structural shocks is of interest. Overidentified SVARs with external instruments can be easily tested in our setup. The suggested method is applied to investigate empirically whether commonly employed measures of macroeconomic and financial uncertainty respond on-impact, other than with lags, to business cycle fluctuations in the U.S. in the period after the Global Financial Crisis. To do so, we employ two external instruments to identify the real economic activity shock in a partial shock approach.

The recent empirical literature that uses Structural Vector Autoregressions (SVAR) has shown that productivity shocks identified using long--run restrictions lead to a persistent and significant decline in hours worked. This evidence calls into question standard RBC models in which a positive technology shock leads to a rise in hours. In this paper, we estimate and test a standard RBC model using Indirect Inference on impulse responses of hours worked after technology and non-technology shocks. We find that this model is not rejected by the data and is able to produce impulse responses in SVAR from simulated data similar to impulse responses in SVAR from actual data. Moreover, technology shocks represent the main contribution to the variance of the business cycle component of output under the estimated DSGE model. Our results suggest that we do not necessarily need DSGE models with a fall in hours to reproduce the results deriving from SVAR models.