What are shocks capturing in DSGE modelling? Structure versus misspecification
Recent tendency in academic work and at central banks is to develop and estimate large DSGE models for policy analysis and forecasting. These models typically have many shocks (e.g. Smets and Wouters, 2003 and Adolfson, Laseen, Linde and Villani, 2005). On the other hand, empirical studies suggest that few large shocks are sufficient to capture the covariance structure of macro data (Giannone, Reichlin and Sala, 2003 and 2005, Uhlig, 2004, Stock and Watson, 2005). This paper explores the hypothesis that many shocks in DSGE modeling are needed to fit the data in presence of misspecification and measurement problems. We consider an alternative approach which models explicitly the statistical agency along the lines of Sargent (1989). We show that, once we allow for measuring error, only few structural shocks remain significant in standard DSGE models.
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