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 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 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...

We investigate the effect of house prices on household borrowing using administrative mortgage data from the United Kingdom and a new empirical approach. The data contain household-level information on house prices and borrowing in a panel of homeowners, who refinance at regular and...

Using a novel source of quasi-experimental variation in interest rates, we study the response of household debt and intertemporal consumption allocation to interest rates. We also develop a new approach to structurally estimate the Elasticity of Intertemporal Substitution (EIS). In the United...

We exploit a unique dataset that features both un-intermediated mortgage requests and independent responses from multiple banks to each request. We show that households typically are not prudent risk managers, but prioritize minimizing current mortgage payments over insurance against future rate...

Using matched microdata for the UK, I estimate two distinct channels via which credit supply shocks affect mortgage debt: one that operates through price conditions in credit markets; and another that operates through non-price credit conditions and affects the quantity of credit supplied by...

This paper studies supply-side product pricing when consumers underreact to non-salient fees. Using comprehensive data on issued and offered mortgages in the UK, I document that lenders differ substantially in the fees they charge, and that borrowers appear less overall cost-sensitive to...

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...

Is human or artificial intelligence more conducive to a stable financial system? To answer this question, we compare human and artificial intelligence with respect to several facets of their decision-making behaviour. On that basis, we characterise possibilities and challenges in designing...