Effects of incorrect specification on the finite sample properties of full and limited information estimators in DSGE models

Sebastian Giesen and Rolf Scheufele

Journal of Macroeconomics, 2016, vol. 48, issue C, 1-18

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 parameter 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.

Keywords: FIML; (CU)GMM; Finite sample bias; Misspecification; Monte Carlo; DSGE (search for similar items in EconPapers)
JEL-codes: C26 C36 C51 E17 (search for similar items in EconPapers)
Date: 2016
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Related works:
Working Paper: Effects of Incorrect Specification on the Finite Sample Properties of Full and Limited Information Estimators in DSGE Models (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmacro:v:48:y:2016:i:c:p:1-18

DOI: 10.1016/j.jmacro.2016.01.002

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