Vector autoregression models are commonly used to study the impact of monetary policy on economies. However, these models have limitations due to the amount of data used. Factor models, which summarize data using principal components, have been suggested as a solution. In a study using Swedish data, combining factor models with VAR models improved the understanding of monetary policy effects. However, these factor-augmented models did not significantly improve forecasting accuracy compared to standard models.