Using a model of deterministic structural change, we revisit several topics in inflation dynamics explored previously using stochastic, time - varying parameter models. We document significant reductions in inflation persistence and predictability. We estimate that changes in the volatility of...

We consider the best way to extract timely signals from newspaper text and use them to forecast macroeconomic variables using three popular UK newspapers that collectively represent UK newspaper readership in terms of political perspective and editorial style. We find that newspaper text can...

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...

A financial conditions index (FCI) is designed to summarise the state of financial markets. Two are constructed with UK data. The first is the first principal component of a set of financial indicators. The second comes from a new approach taking information from a large set of macroeconomic...

This paper provides an approach to estimation and inference for non-linear conditional mean panel data models, in the presence of cross-sectional dependence. We modify the common correlated effects (CCE) correction of Pesaran (2006) to filter out the interactive unobserved multifactor structure....

We re-examine the great ratios associated with balanced growth models and ask whether they have remained constant over time. Having first looked at whether Kaldor's stylised facts still apply to the UK data, we employ a nonparametric methodology that allows for slowly varying coefficients to...

DSGE models are of interest because they offer structural interpretations, but are also increasingly used for forecasting. Estimation often proceeds by methods which involve building the likelihood by one-step ahead (h=1) prediction errors. However in principle this can be done using different...

This paper uses kernel methods to estimate a seven variable time-varying (TV) vector autoregressive (VAR) model on the US data set constructed by Smets and Wouters. We use an indirect inference method to map from this TV VAR to time variation in implied Dynamic Stochastic General Equilibrium...

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 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...