Trend-Cycle Decomposition and Forecasting Using Bayesian Multivariate Unobserved Components

We propose a generalized multivariate unobserved components model to decompose macroeconomic data into trend and cyclical components. We then forecast the series using Bayesian methods. We document that a fully Bayesian estimation, that accounts for state and parameter uncertainty, consistently dominates out-of-sample forecasts produced by alternative multivariate and univariate models. In addition, allowing for stochastic volatility components in variables improves forecasts. To address data limitations, we exploit cross-sectional information, use the commonalities across variables, and account for both parameter and state uncertainty. Finally, we find that an optimally pooled univariate model outperforms individual univariate specifications, andperforms generally closer to the benchmark model.