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The role of trend inflation shocks for the U.S. macroeconomic dynamics is investigated by estimating two DSGE models of the business cycle. Policymakers are assumed to be concerned with a time-varying inflation target, which is modeled as a persistent and stochastic process. The identification of trend inflation shocks (as opposed to a number of alternative innovations) is achieved by exploiting the measure of trend inflation recently proposed by Aruoba and Schorfheide (2011). Our main findings point to a substantial contribution of trend inflation shocks for the volatility of inflation and the policy rate. Such contribution is found to be time dependent and highest during the mid-1970s to mid-1980s.

This volume of Advances in Econometrics is devoted to dynamic stochastic general equilibrium (DSGE) models, which have gained popularity in both academic and policy circles as a theoretically and methodologically coherent way of analyzing a variety of issues in empirical macroeconomics. The volume is divided into two parts. The first part covers important topics in DSGE modeling and estimation practice, including the modeling and role of expectations, the study of alternative pricing models, the problem of non-invertibility in structural VARs, the possible weak identification in new open economy macro models, and the modeling of trend inflation. The second part is devoted to innovations in econometric methodology. The papers in this section advance new techniques for addressing key theoretical and inferential problems and include discussion and applications of Laplace-type, frequency domain, empirical likelihood, and method of moments estimators.

This paper compares alternative estimation procedures for multi-level factor models which imply blocks of zero restrictions on the associated matrix of factor loadings. We suggest a sequential least squares algorithm for minimizing the total sum of squared residuals and a two-step approach based on canonical correlations that are much simpler and faster than Bayesian approaches previously employed in the literature. An additional advantage is that our approaches can be used to estimate more complex multi-level factor structures where the number of levels is greater than two. Monte Carlo simulations suggest that the estimators perform well in typical sample sizes encountered in the factor analysis of macroeconomic data sets. We apply the methodologies to study international comovements of business and financial cycles.