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Back to square one: identification issues in DSGE models

  • Fabio Canova

    (UPF and CEPR)

  • Luca Sala

    (Università Bocconi, IGIER)

We investigate identifiability issues in DSGE models and their consequences for parameter estimation and model evaluation when the objective function measures the distance between estimated and model impulse responses. We show that observational equivalence, partial and weak identification problems are widespread, that they lead to biased estimates, unreliable t-statistics and may induce investigators to select false models. We examine whether different objective functions affect identification and study how small samples interact with parameters and shock identification. We provide diagnostics and tests to detect identification failures and apply them to a state-of-the-art model

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 196.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:196
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