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Effects of Incorrect Specification on the Finite Sample Properties of Full and Limited Information Estimators in DSGE Models

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  • Sebastian Giesen
  • R. Scheufele

Abstract

In this paper we analyze the small sample properties of full information and limited information estimators in a potentially misspecified DSGE model. Therefore, we conduct a simulation study based on a standard New Keynesian model including price and wage rigidities. We then study the effects of omitted variable problems on the structural parameters estimates of the model. We find that FIML performs superior when the model is correctly specified. In cases where some of the model characteristics are omitted, the performance of FIML is highly unreliable, whereas GMM estimates remain approximately unbiased and significance tests are mostly reliable.

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Bibliographic Info

Paper provided by Halle Institute for Economic Research in its series IWH Discussion Papers with number 8.

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Date of creation: Apr 2013
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Handle: RePEc:iwh:dispap:8-13

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Keywords: FIML; GMM; finite sample bias; misspecification; Monte Carlo; DSGE;

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