We estimate demand, supply, monetary, investment and financial shocks in a VAR identified with a minimum set of sign restrictions on US data. We find that financial shocks are major drivers of fluctuations in output, stock prices and investment but have a limited effect on inflation. In a second...

We propose a new VAR identfication scheme that enables us to disentangle labor supply shocks from wage bargaining shocks. Identification is achieved by imposing robust signrestrictions that are derived from a New Keynesian model with endogenous labor force participation. According to our...

We investigate the macroeconomic consequences of .uctuations in the effectiveness of the labor-market matching process with a focus on the Great Recession. We conduct our analysis in the context of an estimated medium-scale DSGE model with sticky prices and equilibrium search unemployment that...

A large decline in the e¢ ciency of the U.S. labor market in matching unemployed workers and vacant jobs has been documented during the Great Recession. We use a simple New Keynesian model with search and matching frictions in the labor market to study the propagation of matching e¢ ciency...

Can long-run identified structural vector autoregressions (SVARs) discriminate between competing models in practice? Several authors have suggested SVARs fail partly because they are fiite-order approx-imations to infinite-order processes. We estimate vector autoregressive moving average (VARMA)...

Experimental studies on decision making based on advice received from others find that the weight put on the advice is negatively related to the distance between the advice and the decisionmaker's initial opinion. In this paper, we show that the distance effect can follow from rational signal...

We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random...

We propose a novel Bayesian model combination approach where the combination weights depend on the past forecasting performance of the individual models entering the combination through a utility-based objective function. We use this approach in the context of stock return predictability and...

Using a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying...

Small or medium-scale VARs are commonly used in applied macroeconomics for forecasting and evaluating the shock transmission mechanism. This requires the VAR parameters to be stable over the evaluation and forecast sample, or to explicitly consider parameter time variation. The earlier...