This paper introduces an adaptive algorithm for time-varying autoregressive models in presence of heavy tails. The evolution of the parameters is driven by the score of the conditional distribution. The resulting model is observation-driven and is estimated by classical methods. Meaningful...

We propose a mixed‑frequency regression prediction approach that models a time‑varying trend, stochastic volatility and fat tails in the variable of interest. The coefficients of high‑frequency indicators are regularised via a shrinkage prior that accounts for the grouping structure and...

We forecast CPI inflation in the United Kingdom up to one year ahead using a large set of monthly disaggregated CPI item series combined with a wide set of forecasting tools, including dimensionality reduction techniques, shrinkage methods and non-linear machine learning models. We find that...

The Bank of England has constructed a 'suite of statistical forecasting models' (the 'Suite') providing judgement-free statistical forecasts of inflation and output growth as one of many inputs into the forecasting process, and to offer measures of relevant news in the data. The Suite combines a...

Density forecast combinations are becoming increasingly popular as a means of improving forecast ‘accuracy', as measured by a scoring rule. In this paper we generalise this literature by letting the combination weights follow more general schemes. Sieve estimation is used to optimise the score...

We develop early warning models for financial crisis prediction using machine learning techniques on macrofinancial data for 17 countries over 1870–2016. Machine learning models mostly outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of...

We propose a Release-Augmented Dynamic Factor Model (RA-DFM) that allows to quantify the role of a country's data flow in nowcasting both early GDP releases, and subsequent revisions of official estimates. We use the RA-DFM to study UK GDP early revision rounds, and assemble a comprehensive and...

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...

We introduce machine learning in the context of central banking and policy analyses. Our aim is to give an overview broad enough to allow the reader to place machine learning within the wider range of statistical modelling and computational analyses, and provide an idea of its scope and...

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...