We use a threshold vector autoregression to study the effects of monetary policy shocks on the US. Depending on the level of inflation we note important regime dependence in the inflation response to monetary policy shocks.

A trade-off exists between the Gelfand and Dey (1994) and Chib (1995) methods to calculate the marginal likelihood in Bayesian estimation. Using the Markov Chain Monte Carlo method, we demonstrate that the performance of the two methods is fairly close.

The debt crisis in the euro area led to obvious changes in the structure of euro area bond markets. To model the process of disintegration that has taken place as a result of this crisis, this analysis uses a dynamic factor model with time-varying loadings and two factors. While some core...

This paper proposes a couple of new methods to compute the news impact curve for stochastic volatility (SV) models. The new methods incorporate the joint movement of return and volatility, which has been ignored by the extant literature. The first method employs the Bayesian Markov chain Monte...

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.

This paper characterizes the co-movements in commodity prices with a dynamic latent factor model that decomposes commodity returns into global, sectoral, and idiosyncratic components. The results indicate that global and sectoral factors are important sources of co-movements in commodity...

This paper introduces nowcasting causality as the mixed-frequency version of instantaneous causality. We analyze the relationship between nowcasting and Granger causality in a mixed-frequency VAR and illustrate its impact on the significance of high-frequency variables in mixed-frequency...

We consider the use of information criteria (IC) on the basis of a semiparametric seasonal error correction model for selecting seasonal cointegrating ranks. Some limit properties of the IC are considered and, through a small Monte Carlo simulation, we evaluate the performance of the IC.

This note shows how conditional forecasts from identified VAR models can be computed using Kalman filtering techniques. These techniques are nowadays routine for applied macroeconomists, and hence the computation of conditional forecasts using these methods are simple to implement.

Structural VAR studies disagree with narrative accounts about the history of monetary policy disturbances. We investigate whether employing the narrative monetary shocks as a proxy variable in a VAR model aligns both shock series. We find that it does not.