Global Identification of Linearized DSGE Models
NBP Working Paper No. 170
40 Pages Posted: 26 Feb 2014
Date Written: February 24, 2014
This paper introduces a time domain framework to analyze global identification of stochastically nonsingular DSGE models. A formal identification condition is established that relies on the restrictions linking the observationally equivalent minimal state space representations and on the inherent constraints imposed by them on deep model parameters. We next develop an algorithm that checks global identification by searching for observationally equivalent model parametrizations. The algorithm is efficient as the identification conditions it employs shrink considerably the space of candidate deep parameter points and does not require solving the model at each of these points. We also derive two complementary necessary conditions for global identification. Their usefulness and the working of the algorithm are illustrated with an example.
Keywords: global identification, DSGE models
JEL Classification: C13, C51, E32
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