Dynamic Deconvolution with a Large Number of States

Dynamic factor models are widely utilized for short-term forecasting due to their robust predictive capabilities. However, significant challenges arise in parameter identification and estimation, particularly when dealing with serially dependent inputs and a large number of states (K). This thesis addresses these challenges by proposing a solution for the identification and computational problems in dynamic factor models when K is large.