We combine the factor augmented VAR framework with recently developed estimation and identification procedures for sparse dynamic factor models. Working with a sparse hierarchical prior distribution allows us to discriminate between zero and non-zero factor loadings. The non-zero loadings identify the unobserved factors and provide a meaningful economic interpretation for them. Applying our methodology to US macroeconomic data reveals indeed a high degree of sparsity in the data. We use the estimated FAVAR to study the effect of a monetary policy shock and a shock to the term premium. Factors and specific variables show sensible responses to the identified shocks.