Factor models are used to simplify large datasets by identifying common patterns. This study introduces a new approach where the factors change over time, capturing evolving relationships in the data. By analyzing economic data with smooth transitions, the researchers show that this dynamic factor model can accurately estimate common components. The method is tested through simulations, demonstrating its effectiveness in capturing complex patterns in time series data.