Working Paper: CEPR ID: DP7712
Authors: Vasco Curdia; Ricardo Reis
Abstract: The dynamic stochastic general equilibrium (DSGE) models that are used to study business cycles typically assume that exogenous disturbances are independent autoregressions of order one. This paper relaxes this tight and arbitrary restriction, by allowing for disturbances that have a rich contemporaneous and dynamic correlation structure. Our first contribution is a new Bayesian econometric method that uses conjugate conditionals to make the estimation of DSGE models with correlated disturbances feasible and quick. Our second contribution is a re-examination of U.S. business cycles. We find that allowing for correlated disturbances resolves some conflicts between estimates from DSGE models and those from vector autoregressions, and that a key missing ingredient in the models is countercyclical fiscal policy. According to our estimates, government spending and technology disturbances play a larger role in the business cycle than previously ascribed, while changes in markups are less important.
Keywords: Bayesian estimation; DSGE; Robustness
JEL Codes: E1; E3
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Government spending increases following productivity declines (H59) | output (C67) |
| Productivity shocks (O49) | delayed increases in government spending (E62) |
| delayed increases in government spending (E62) | output (C67) |
| correlated disturbances (C32) | dynamics of the model (C69) |
| independent disturbances (C62) | traditional estimates of intertemporal elasticity of substitution (D15) |
| Government spending and technology disturbances (O49) | economic fluctuations (E32) |
| markup changes (Y20) | diminished impact on economic fluctuations (F69) |