Thesis addresses parameter identification and estimation challenges in dynamic factor models with many states, suggesting a novel solution.
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.
No takes yet. Share an insight, caveat, or question.
Zhaoyang Xu (2024) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: