Notwithstanding the increased use of estimated dynamic stochastic general equilibrium
(DSGE) models over the last decade, structural vector autoregressive (VAR) models …

Conventional structural vector autoregressive (SVAR) models with Gaussian errors are not
identified, and additional identifying restrictions are needed in applied work. We show that …

Is credit expansion a sign of desirable financial deepening or the prelude to an inevitable
bust? We study this question in modern US data using a structural VAR model of 10 monthly …

Structural vector‐autoregressive models are potentially very useful tools for guiding both
macro‐and microeconomic policy. In this study, we present a recently developed method for …

It is argued that in structural vector autoregressive (SVAR) analysis a Markov regime
switching (MS) property can be exploited to identify shocks if the reduced form error …

In structural vector autoregressive (SVAR) analysis a Markov regime switching (MS) property
can be exploited to identify shocks if the reduced form error covariance matrix varies across …

Vector autoregressive (VAR) models have a long tradition as tools for multiple time-series
analysis (eg Quenouille 1957). Being linear models, they are relatively easy to work with …

Long‐run restrictions have been used extensively for identifying structural shocks in vector
autoregressive (VAR) analysis. Such restrictions are typically just‐identifying but can be …

I propose to identify an SVAR, up to shock ordering, using the autocovariance structure of
the squared innovations implied by an arbitrary stochastic process for the shock variances …

When quantifying the importance of supply and demand for oil price fluctuations, a wide
range of estimates have been reported. Models identified via a sharp upper bound on the …