This article suggests a new approach to approximating moments for nonlinear DSGE models. This approach is fast and sufficiently accurate to estimate nonlinear DSGE models. A small financial DSGE model is repeatedly es...This article suggests a new approach to approximating moments for nonlinear DSGE models. This approach is fast and sufficiently accurate to estimate nonlinear DSGE models. A small financial DSGE model is repeatedly estimated by several modifications of the suggested approach. Approximations of the moments are close to the results of the large sample Monte Carlo estimation. The quality of parameter estimation using our suggested approach is close to the Central Difference Kalman Filter （CDKF）; and our suggested approach is much faster.展开更多
文摘This article suggests a new approach to approximating moments for nonlinear DSGE models. This approach is fast and sufficiently accurate to estimate nonlinear DSGE models. A small financial DSGE model is repeatedly estimated by several modifications of the suggested approach. Approximations of the moments are close to the results of the large sample Monte Carlo estimation. The quality of parameter estimation using our suggested approach is close to the Central Difference Kalman Filter （CDKF）; and our suggested approach is much faster.
