Vector autoregression (VAR) models are widely used in an attempt to identify and measure the effect of monetary policy shocks on an economy and to forecast economic times series. However, the sparse information sets used in the VAR approach have been subject to criticism and in recent decades, the use of factor models as a means of dimension reduction has been a subject of greater focus. The method of summarizing information contained in a large set of macroeconomic time series by principal components, and use these as regressors in VAR models, has been pointed out as a potential solution to the problems of limited information and estimation of too many parameters. This paper combines the standard VAR methodology with dynamic factor analysis on Swedish data for two purposes, to assess the effects of monetary policy shocks and to examine the forecasting properties. Latent factors estimated by the principal components method are in this study found to contribute to a more coherent picture in line with economic theory, when examining monetary policy shocks to the Swedish economy. The factor-augmented models can on the other hand not be shown to increase the forecasting accuracy to a great extent compared to standard models.