Trend Mis-specifications and Estimated Policy Implications in DSGE Models

Extracting a trend component from nonstationary data is one of the first challenges in estimating a DSGE model. The misspecification of the component can distort structural parameter estimates and translate into a bias in policy-relevant statistic estimates. This paper investigates how important this bias is to estimated policy implications within a DSGE framework. The quantitative results suggest the bias in parameter estimates due to trend misspecification can result in significant inaccuracies in estimating statistics of interest. This then misleads policy conclusions. Particularly, a misspecified model is estimated using a deterministic-trend specification when the true process is a random-walk with drift.