In this paper, we provide evidence that fat tails and stochastic volatility can be important in improving in-sample fit and out-of-sample forecasting performance. Specifically, we construct a VAR model where the orthogonalised shocks feature Student’s t distribution and time-varying variance....

In this note we present an updated algorithm to estimate the VAR with stochastic volatility proposed in Mumtaz (2018). The model is re-written so that some of the Metropolis Hastings steps are avoided.

This paper introduces a exible local projection that generalises the model by Jordà (2005) to a non-parametric setting using Bayesian Additive Regression Trees. Monte Carlo experiments show that our BART-LP model is able to capture non-linearities in the impulse responses. Our first application...

This paper introduces a VAR with stochastic volatility in mean where the residuals of the volatility equations and the observation equations are allowed to be correlated. This implies that exogeneity of shocks to volatility is not assumed apriori and structural shocks can be identified ex-post...

This paper extends the procedure developed by Jurado et al. (2015) to allow the estimation of measures of uncertainty that can be attributed to specific structural shocks. This enables researchers to investigate the "origin" of a change in overall macroeconomic uncertainty. To demonstrate the...

In this paper we extend the Bayesian Proxy VAR to incorporate time variation in the parameters. A Gibbs sampling algorithm is provided to approximate the posterior distributions of the model's parameters. Using the proposed algorithm, we estimate the time-varying effects of taxation shocks in...

This paper introduces a VAR with stochastic volatility in mean where the residuals of the volatility equations and the observation equations are allowed to be correlated. This implies that exogeneity of shocks to volatility is not assumed apriori and structural shocks can be identified ex-post...

This paper extends the procedure developed by Jurado et al. (2015) to allow the estimation of measures of uncertainty that can be attributed to specific structural shocks. This enables researchers to investigate the "origin" of a change in overall macroeconomic uncertainty. To demonstrate the...

In this paper we extend the Bayesian Proxy VAR to incorporate time variation in the parameters. A Gibbs sampling algorithm is provided to approximate the posterior distributions of the model's parameters. Using the proposed algorithm, we estimate the time-varying effects of taxation shocks in...

In this paper, we provide evidence that fat tails and stochastic volatility can be important in improving in-sample fit and out-of-sample forecasting performance. Specifically, we construct a VAR model where the orthogonalised shocks feature Student's t distribution and time-varying variance. We...