This paper provides a detailed assessment of the real-time forecast accuracy of a wide range of vector autoregressive models (VAR) that allow for both structural change and indicators sampled at different frequencies. We extend the literature by evaluating a mixed-frequency time-varying...

We develop a small-scale dynamic factor model for the Swiss economy allowing for non-linearities by means of a two-state Markov-chain. The selection of an appropriate set of indicators utilizes a combinatorial algorithm. The model's forecasting performance is as good as that of peers with richer...

There is a growing interest in allowing for asymmetry in the density forecasts of macroeconomic variables. In multivariate time series, this can be achieved with a copula model, where both serial and cross-sectional dependence is captured by a copula function, and the margins are nonparametric....

In this article we present four diversified approaches to forecasting main macroeconomic variables without a priori assumptions concerning causality. We include tendency survey data in both the Bayesian averaging of classical estimates (BACE) and the dynamic factor models (DFM) frameworks. With...

We show that the single-index dynamic factor model developed by Aruoba and Diebold (Am Econ Rev, 100:20-24, 2010) to construct an index of US business cycle conditions is also very useful for forecasting US GDP growth in real time. In addition, we adapt the model to include survey data and...