This paper estimates a dynamic factor model (DFM) for nowcasting Canadian gross domestic product. The model is estimated with a mix of soft and hard indicators, and it features a high share of international data. The model is then used to generate nowcasts, predictions of the recent past and...

Bayesian predictive synthesis (BPS) is a method of combining predictive distributions based on agent opinion analysis theory, which encompasses many common approaches to combining density forecasts. The key ingredient in BPS is a synthesis function. This is typically specified parametrically as...

The estimation of dynamic factor models for large sets of variables has attracted considerable attention recently, due to the increased availability of large datasets. In this paper we propose a new parametric methodology for estimating factors from large datasets based on state space models and...

The estimation of structural dynamic factor models (DFMs) for large sets of variables is attracting considerable attention. In this paper we briefly review the underlying theory and then compare the impulse response functions resulting from two alternative estimation methods for the DFM....

The macroeconomy is a complicated dynamic system with significant uncertainties that make modelling difficult. Consequently, decision-makers consider multiple models that provide different predictions and policy recommendations and then synthesize that information into a policy decision. We use...

This paper analyses the use of factor analysis for instrumental variable estimation when the number of instruments tends to infinity. In particular, we focus on situations where many weak instruments exist and/or the factor structure is weak. Theoretical results, simulation experiments and...