We present a global sensitivity analysis that quantifies the impact of parameter uncertainty on model outcomes. Specifically, we propose Sobol’ indices to establish an importance ranking of parameters and univariate effects to determine the direction of the impact. Sobol’ indices are based on variance decomposition and are able to fully capture non-linearities and identify interactions between parameters. Univariate effects are computed as conditional expectations. We employ the state-of-the-art approach for global sensitivity analysis by constructing a polynomial chaos expansion of the model from a limited number of evaluations. Sobol’ indices and univariate effects are then calculated analytically from the polynomial coefficients. We apply this analysis to a standard real-business-cycle model and compare it to traditional local sensitivity analysis approaches.