Evaluating the strength of identification in DSGE models. An a priori approach

By Nikolay Iskrev

http://d.repec.org/n?u=RePEc:ptu:wpaper:w201032&r=dge

This paper presents a new approach to parameter identification analysis in DSGE models wherein the strength of identification is treated as property of the underlying model and studied prior to estimation. The strength of identification reflects the empirical importance of the economic features represented by the parameters. Identification problems arise when some parameters are either nearly irrelevant or nearly redundant with respect to the aspects of reality the model is designed to explain. The strength of identification therefore is not only crucial for the estimation of models, but also has important implications for model development. The proposed measure of identification strength is based on the Fisher information matrix of DSGE models and depends on three factors: the parameter values, the set of observed variables and the sample size. By applying the proposed methodology, researchers can determine the effect of each factor on the strength of identification of individual parameters, and study how it is related to structural and statistical characteristics of the economic model. The methodology is illustrated using the medium-scale DSGE model estimated in Smets and Wouters (2007).

Every good simulation exercise includes a sensitivity analysis on the calibrated parameter values. This paper presents an interesting way to which parameters are of little importance and which are crucial and thus should be calibrated or estimate with extra care.