Macroeconomic practitioners frequently work with multivariate time series models such as VARs, factor augmented VARs as well as time-varying parameter versions of these models (including variants with multivariate stochastic volatility). These models have a large number of parameters and, thus,...

In this paper we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also...

This paper develops methods for estimating and forecasting in Bayesian panel vector autoregressions of large dimensions with time-varying parameters and stochastic volatility. We exploit a hierarchical prior that takes into account possible pooling restrictions involving both VAR coefficients...

This paper proposes a mean field variational Bayes algorithm for efficient posterior and predictive inference in time-varying parameter models. Our approach involves: i) computationally trivial Kalman filter updates of regression coefficients, ii) a dynamic variable selection prior that removes...

We use factor augmented vector autoregressive models with time-varying coefficients to construct a financial conditions index. The time-variation in the parameters allows for the weights attached to each financial variable in the index to evolve over time. Furthermore, we develop methods for...

We develop methods for Bayesian model averaging (BMA) or selection (BMS) in Panel Vector Autoregressions (PVARs). Our approach allows us to select between or average over all possible combinations of restricted PVARs where the restrictions involve interdependencies between and heterogeneities...

This paper proposes a variational Bayes algorithm for computationally efficient posterior and predictive inference in time-varying parameter (TVP) models. Within this context we specify a new dynamic variable/model selection strategy for TVP dynamic regression models in the presence of a large...

This paper considers how an investor in foreign exchange markets might exploit predictive information in macroeconomic fundamentals by allowing for switching between multivariate time series regression models. These models are chosen to reflect a wide array of established empirical and...