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 ≥ 1 structural shocks, where r ≥ g. We endow the SVAR with an auxiliary statistical model...

Beaudry and Portier (2006) provide support for the "news view" of the business cycle, using a vector error correction model. We show that this result hinges on a cointegrating relationship between TFP and stock prices that is not stationary, thus making the estimates not reliable. If we alter...

The price puzzle is the association in a structural vector autoregression (SVAR) of a contractionary shock to monetary policy with persistent increases in the price level. Various explanations have been investigated separately in the framework of small SVARs without any common set of variables...

In this paper we extend the standard Blanchard-Quah decomposition to enable fluctuations in aggregate demand to have a long-run impact on the productive capacity of the economy through hysteresis effects. These demand shocks are found to be quantitatively important in the US, in particular if...

This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model.It establishes that systematically different dynamic restrictions are imposed whenthe ratio of volatilities is time-varying....

This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model. It establishes that systematically different dynamic restrictions are imposed when the ratio of volatilities is time-varying....

We propose to add ranking restrictions on impulse-responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully...

We propose a blended approach which combines identification via heteroskedasticity with the widely used methods of sign restrictions, narrative restrictions, and external instruments.Since heteroskedasticity in the reduced form can be exploited to point identify a set of orthogonal shocks, its...

In structural vector autoregressive analysis identifying the shocks of interest via heteroskedasticity has become a standard tool. Unfortunately, the approaches currently used for modelling heteroskedasticity all have drawbacks. For instance, assuming known dates for variance changes is often...