A Note On Identification Patterns In DSGE Models
This paper comments on selected aspects of identification issues of DSGE models. It suggests the Singular Value Decomposition (SVD) as a useful tool for detecting local weak and non-identification. This decomposition is useful for checking rank conditions of identification, identification strength, and it also offers parameter space 'Identification patterns'. With respect to other methods of identification the singular value decomposition is particularly easy to apply and offers an intuitive interpretation. The authors suggest a simple algorithm for analyzing identification and an algorithm for finding a set of the most identifiable set of parameters. They also demonstrate that the use of bivariate and multiple correlation coefficients of parameters provide only limited check of identification problems.