The existing literature on large dynamic factor models invariably assumes that the cross sectional covariance matrix is diagonal. This is due to the curse of dimensionality which means that many parameters need to be estimated for large data sets. This paper introduces a novel maximum likelihood...

Until recently literature on dynamic factor models invariably assumed that the cross sectional error covariance matrix is diagonal. This is due to the many parameters which need to be estimated for large data sets, which is known as the curse of dimensionality. Spencer (2024) introduced a novel...

The existing literature on large dynamic factor models invariably assumes that the cross sectional covariance matrix is diagonal. This is due to the curse of dimensionality which means that many parameters need to be estimated for large data sets. This paper introduces a novel maximum likelihood...