Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
The issue of identification arises whenever structural models are estimated. Lack of identification means that the empirical implications of some model parameters are either undetectable or indistinguishable from the implications of other parameters. Therefore, identifiability must be verified prior to estimation. This paper provides a simple method for conducting local identification analysis in linearized DSGE models, estimated in both full and limited information settings. In addition to establishing which parameters are locally identified and which are not, researchers can determine whether the identification failures are due to data limitations, such as lack of observations for some variables, or whether they are intrinsic to the structure of the model. The methodology is illustrated using a medium-scale DSGE model.