Approximate factor models and their extensions are widely used in economic analysis and forecasting due to their ability to extracting useful information from a large number of relevant variables. In these models, candidate predictors are typically subject to some common components. In this...

The well-known problem of too many instruments in dynamic panel data GMM is dealt with in detail in Roodman (2009, Oxford Bull. Econ. Statist.). The present paper goes one step further by providing a solution to this problem: factorisation of the standard instrument set is shown to be a valid...

Quarterly GDP figures usually are published with a delay of some weeks. A common way to generate GDP series of higher frequency, i.e. to nowcast GDP, is to use available indicators to calculate a single index by means of a common factor derived from a dynamic factor model (DFM). This paper deals...

In this paper, a scaled independent component analysis (sICA) method is proposed for finding potential factors with more predictive power. The core idea is to improve the predictive effect of the model by giving more weight to those variables with stronger predictive power before estimating the...

Portfolio selection is often faced with large noisy data sets of strongly correlated asset returns, and so is prone to unstable portfolio weights and serious estimation error. To attenuate these problems, this paper proposes a new latent factor model equipped with both a suitable robust...

A number of recent studies in the economics literature have focused on the usefulness of factor models in the context of prediction using "big data". In this paper, our over-arching question is whether such "big data" are useful for modelling low frequency macroeconomic variables such as...