This paper introduces the Bank of England's new forecasting platform and provides examples of how it can be applied to practical forecasting problems. The platform consists of four components: COMPASS, a structural central organising model; a suite of models, used to fill in the gaps in the...

We explore the effects of forward guidance at the zero lower bound when there is uncertainty over the lift-off date arising from: (i) the imperfect credibility of time-inconsistent forward-guidance promises; (ii) incomplete communication. We use a simple New Keynesian model to demonstrate that a...

This paper studies how non-Gaussian shocks affect risk premia in DSGE models approximated to second and third order. Based on an extension of the work by Schmitt-Grohe and Uribe to third order, we derive propositions for how rare disasters, stochastic volatility, and GARCH affect any risk premia...

Long-term real interest rates across the world have fallen by about 450 basis points over the past 30 years. The co-movement in rates across both advanced and emerging economies suggests a common driver: the global neutral real rate may have fallen. In this paper we attempt to identify which...

We propose a generic workflow for the use of machine learning models to inform decision making and to communicate modelling results with stakeholders. It involves three steps: (1) a comparative model evaluation, (2) a feature importance analysis and (3) statistical inference based on Shapley...

Forecasts play a critical role at inflation-targeting central banks, such as the Bank of England. Breaks in the forecast performance of a model can potentially incur important policy costs. Commonly used statistical procedures, however, implicitly put a lot of weight on type I errors (or false...

By employing large panels of survey data for the UK economy, we aim at reviewing linear approaches for regularisation and dimension reduction combined with techniques from the machine learning literature, like Random Forests, Support Vector Regressions and Neural Networks for forecasting GDP...

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...

We consider time series forecasting in the presence of ongoing structural change where both the time-series dependence and the nature of the structural change are unknown. Methods that downweight older data, such as rolling regressions, forecast averaging over different windows and exponentially...

Default correlation is a key driver of credit risk. In the Basel regulatory framework it is measured by the asset value correlation parameter. Though past studies suggest that the parameter is over-calibrated for mortgages — generally the largest asset class on banks' balance sheets — they...