View article

This paper considers inference in log‐linearized dynamic stochastic general equilibrium (DSGE) models with weakly (including un‐) identified parameters. The framework allows for analysis using only part of the spectrum, say at the business cycle frequencies. First, we characterize weak identification from a frequency domain perspective and propose a score test for the structural parameter vector based on the frequency domain approximation to the Gaussian likelihood. The construction heavily exploits the structures of the DSGE solution, the score function, and the information matrix. In particular, we show that the test statistic can be represented as the explained sum of squares from a complex‐valued Gauss–Newton regression, where weak identification surfaces as (imperfect) multicollinearity. Second, we prove that asymptotically valid confidence sets can be obtained by inverting this test statistic and using chi …