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

Sebastian Giesen and Rolf Scheufele

No 8/2013, IWH Discussion Papers from Halle Institute for Economic Research (IWH)

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.

Keywords: FIML; GMM; finite sample bias; misspecification; Monte Carlo; DSGE; FIML; GMM; Kleinstichprobenverzerrung; Fehlspezifikation; Monte Carlo; DSGE (search for similar items in EconPapers)
JEL-codes: C26 C36 C51 E17 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/75409/1/743968999.pdf (application/pdf)

Related works:
Journal Article: Effects of incorrect specification on the finite sample properties of full and limited information estimators in DSGE models (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:iwhdps:iwh-8-13

Access Statistics for this paper

More papers in IWH Discussion Papers from Halle Institute for Economic Research (IWH) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().