Local identification in DSGE models

DSGE
local identification

Nikolay Iskrev, “Local identification in DSGE models,” Journal of Monetary Economics 57, no.2 (2010): 189-202, doi: 10.1016/j.jmoneco.2009.12.007

Author
Affiliation

Banco de Portugal

Published

April 2010

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Open

Abstract

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.

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@article{ISKREV2010189,
title = {Local identification in DSGE models},
journal = {Journal of Monetary Economics},
volume = {57},
number = {2},
pages = {189-202},
year = {2010},
issn = {0304-3932},
doi = {https://doi.org/10.1016/j.jmoneco.2009.12.007},
url = {https://www.sciencedirect.com/science/article/pii/S0304393209001883},
author = {Nikolay Iskrev},
keywords = {DSGE models, Identification},
abstract = {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.}
}