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...

We present a new method for estimating Bayesian vector auto-regression (VAR) models using priors from a dynamic stochastic general equilibrium (DSGE) model. We use the DSGE model priors to determine the moments of an independent Normal-Wishart prior for the VAR parameters. Two hyper-parameters...

This paper identifies shocks to credit conditions based on aggregate firms' debt composition. I develop a model where firms fund production with bonds and loans. Only financial shocks imply opposite movements in the two types of debt as firms adjust their debt composition to new credit...

This paper quantitatively studies the behaviour of major banks' household deposit funding in the United Kingdom. We estimate a panel of Bayesian vector autoregressive models on a unique data set compiled by the Bank of England, and identify deposit demand and supply shocks, both to individual...

Using novel data and machine learning techniques, we develop an early warning system for bank distress. The main input variables come from confidential regulatory returns, and our measure of distress is derived from supervisory assessments of bank riskiness from 2006 through to 2012. We...

We discuss combining sign restrictions with information in external instruments (proxy variables) to identify structural vector autoregressive (SVAR) models. In one setting, we assume the availability of valid external instruments. Sign restrictions may then be used to identify further...

This paper studies the effects of macroeconomic shocks on business investment in the United Kingdom by filtering a large UK firm-level based dataset of financial accounts into macro-level proxy indicators, and then using these indicators in a Bayesian vector autoregression framework to analyse...

We estimate a time varying parameter structural macroeconomic model of the UK economy, using a Bayesian local likelihood methodology. This enables us to estimate a large open-economy DSGE model over a sample that comprises several different regimes and an incomplete set of data. Our estimation...

This paper proposes a method to interpret factors which are otherwise difficult to assign economic meaning to by utilizing a threshold factor-augmented vector autoregression (FAVAR) model. We observe the frequency of the factor loadings being induced to zero when they fall below the estimated...

Gertler and Karadi combined financial intermediation and credit policy in a DSGE framework. We estimate their model with UK data using Bayesian techniques. To validate the fit, we evaluate the model's empirical properties. Then we analyse the transmission mechanism of the shocks, set to produce...