Sufficient Information in Structural VARs

We derive necessary and sufficient conditions under which a set of variables is informationally sufficient, i.e. contains enough information to estimate the structural shocks with a VAR model. Based on such conditions, we provide a procedure to test for informational sufficiency. If sufficiency is rejected, we propose a strategy to amend the VAR. Our method can be applied to FAVAR models and can be used to determine how many factors to include in such models. We apply our procedure to a VAR including TFP, unemployment and per-capita hours worked. We find that the three variables are not informationally sufficient. When adding missing information, the effects of technology shocks change dramatically

Macroeconomic Uncertainty and Vector Autoregressions

We estimate macroeconomic uncertainty and the effects of uncertainty shocks by means of a new procedure based on standard VARs. Under suitable assumptions, our procedure is equivalent to using the square of the VAR forecast error as an external instrument in a proxy SVAR. We add orthogonality constraints to the standard proxy SVAR identification scheme. We also derive a VAR-based measure of uncertainty. We apply our method to a US data set; we find that uncertainty is mainly exogenous and is responsible of a large fraction of business-cycle fluctuations

Asymmetric Effects of Monetary Policy Easing and Tightening

Monetary policy easing and tightening have asymmetric effects: a policy easing has large effects on prices but small effects on real activity variables. The opposite is found for a policy tightening: large real effects but small effects on prices. Non-linearities are estimated using a new and simple procedure based on linear Structural Vector Autoregressions with exogenous variables (SVARX). We rationalize the results through the lenses of a simple model with downward nominal wage rigidities