Reserving in general insurance is often done using chain-ladder-type methods.  We propose a method aimed at situations where there is a sudden change in the economic environment affecting the policies for all accident years in the reserving triangle.  It is shown that methods for forecasting...

We consider forecasting from age-period-cohort models, as well as from the extended chain-ladder model. The parameters of these models are known only to be identified up to linear trends. Forecasts from such models may therefore depend on arbitrary linear trends. A condition for invariant...

We consider forecasting with factors, variables and both, modeling in-sample using Autometrics so all principal components and variables can be included jointly, while tackling multiple breaks by impulse-indicator saturation.  A forecast-error taxonomy for factor models highlights the impacts...

 We propose a new class of multivariate volatility models utilizing realized measures of asset volatility and covolatility extracted from high-frequency data. Dimension reduction for estimation of large covariance matrices is achieved by imposing a factor structure with time-varying conditional...

We consider the reasons for nowcasting, how nowcasts can be achieved, and the use and timing of information.  The existence of contemporaneous data such as surveys is a major difference from forecasting, but many of the recent lessons about forecasting remain relevant.  Given the extensive...

When breaks occur, equilibrium-correction models (EqCMs) based on cointegration face forecasting problems.  We investigate approaches to alleviate forecast failure following a location shift, including updating, intercept corrections, differencing, and estimating the future impact of an...

We defend the forecasting performance of the FOMC from the recent criticism of Christina and David Romer.  Our argument is that the FOMC forecasts a worst-case scenario that it uses to design decisions that will work well enough (are robust) despite possible misspecification of its model. ...

We develop forecast-error taxonomies when there are unmodeled variables, forecast 'off-line'.  We establish three surprising results.  Even when an open system is correctly specified in-sample with zero intercepts, despite known future values of strongly exogenous variables, changes in...

Functional Signal plus Noise (FSN) models are proposed for analysing the dynamics of a large cross-section of yields or asset prices in which contemporaneous observations are functionally related. The FSN models are used to forecast high dimensional yield curves for US Treasury bonds at the one...