In this paper we analyze the propagation of shocks originating in sectors that are not present in a baseline dynamic stochastic general equilibrium (DSGE) model. Specifically, we proxy the missing sector through a small set of factors, that feed into the structural shocks of the DSGE model to create correlated disturbances. We estimate the factor structure by matching impulse responses of the augmented DSGE model to those generated by an auxiliary model. We apply this methodology to track the effects of oil shocks and housing demand shocks in models without energy and housing sectors.

This paper compares different solution methods for computing the equilibrium of dynamic stochastic general equilibrium (DSGE) models with recursive preferences such as those in Epstein and Zin (1989 and 1991) and stochastic volatility. Models with these two features have recently become popular, but we know little about the best ways to implement them numerically. To fill this gap, we solve the stochastic neoclassical growth model with recursive preferences and stochastic volatility using four different approaches: second- and third-order perturbation, Chebyshev polynomials, and value function iteration. We document the performance of the methods in terms of computing time, implementation complexity, and accuracy. Our main finding is that perturbations are competitive in terms of accuracy with Chebyshev polynomials and value function iteration while being several orders of magnitude faster to run. Therefore, we conclude that perturbation methods are an attractive approach for computing this class of problems.

Does fiscal policy stimulate output? SVARs have been used to address this question but no stylized facts have emerged. We derive analytical relationships between the output elasticities of fiscal variables and fiscal multipliers. We show that standard identification schemes imply different priors on elasticities, generating a large dispersion in multiplier estimates. We then use extra-model information to narrow the set of empirically plausible elasticities, allowing for sharper inference on multipliers. Our results for the U.S. for the period 1947-2006 suggest that the probability of the tax multiplier being larger than the spending multiplier is below 0.5 at all horizons.