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 estimate a novel measure of global Önancial uncertainty (GFU) with a dynamic factor framework that jointly models global, regional, and country-speciÖc factors. We quantify the impact of GFU shocks on global output with a VAR analysis that achieves set-identiÖcation via a combination of...

Has heightened uncertainty been a major contributor to the Great Recession and the slow recovery in the U.S.? To answer this question, we identify exogenous changes in six uncertainty proxies and quantify their contributions to GDP growth and the unemployment rate. Our results are threefold....

To determine whether the real exchange rate is misaligned with respect to its long-run equilibrium is an important issue for policy makers. This paper clarifies and calculates the concept of the equilibrium real exchange rate, using a structural vector autoregression (VAR) model. By imposing...

We study the effects of news shocks on inventory accumulation in a structural VAR framework. We establish that inventories react strongly and positively to news about future increases in total factor productivity. Theory suggests that the transmission channel of news shocks to inventories works...

We introduce a structural quantile vector autoregressive (VAR) model. Unlike standard VAR which models only the average interaction of the endogenous variables, quantile VAR models their interaction at any quantile. We show how to estimate and forecast multivariate quantiles within a recursive...

In this paper we show analytically, with simulation experiments and with actual data that a mismatch between the time scale of a DSGE model and that of the time series data used for its estimation generally creates identfication problems, introduces estimation bias and distorts the results of...

A mismatch between the time scale of a structural VAR (SVAR) model and that of the time series data used for its estimation can have serious consequences for identification, estimation and interpretation of the impulse response functions. However, the use of mixed frequency data, combined with a...