We consider unobserved components time series models where the components are stochastically evolving over time and are subject to stochastic volatility. It enables the disentanglement of dynamic structures in both the mean and the variance of the observed time series. We develop a simulated...

We investigate the relationship between a mutual fund’s variation in systematic risk factor exposures and its future performance. Using a dynamic state space version of Carhart (1997)’s four factor model to capture risk factor variation, we find that funds with volatile risk factor exposures...

This article extends the Factor-Augmented Vector Autoregression Model (FAVAR) to mixed-frequency and incomplete panel data. Within the scope of a fully parametric two-step approach, the alternating application of two expectation-maximization algorithms jointly estimates model parameters and...

Dynamic factor models based on Kalman Filter techniques are frequently used to nowcast GDP. This study deals with the selection of indicators for this practice. We propose a two-tiered mechanism which is shown in a case study to produce more accurate nowcasts than a benchmark stochastic process...

Holston, Laubach and Williams’ (2017) estimates of the natural rate of interest are driven by the downward trending behaviour of ‘other factor’ z(t). I show that their implementation of Stock and Watson’s (1998) Median Unbiased Estimation (MUE) to determine the size of parameter λ(z)...