Based on a General Dynamic Factor Model with infinite-dimensional factor space and MGARCH common shocks, we develop new estimation and forecasting procedures for conditional covariance matrices in high-dimensional time series. The finite-sample performance of our approach is evaluated via Monte...

General dynamic factor models have demonstrated their capacity to circumvent the curse of dimensionality in the analysis of high-dimensional time series and have been successfully considered in many economic and financial applications. Being second-order models, however, they are sensitive to...

This paper shows that the LM statistic for testing first order serial correlation in regression models can be computed using the Kalman Filter. It is shown tha.t when there are missing observations, the LM statistic for this tesi is equivalent to the tesi statistic derived by Robinson (1985)...