Indirect Inference and Small Sample Bias - Some Recent Results
David Meenagh,
A. Patrick Minford and
Yongdeng Xu
No E2023/15, Cardiff Economics Working Papers from Cardiff University, Cardiff Business School, Economics Section
Abstract:
Macroeconomic researchers use a variety of estimators to parameterise their models empirically. One such is FIML; another is a form of indirect inference we term informal under which data features are targeted by the model -i.e. parameters are chosen so that model-simulated features replicate the data features closely. In this paper we show, based on Monte Carlo experiments, that in the small samples prevalent in macro data, both these methods produce high bias, while formal indirect inference, in which the joint probability of the data- generated auxiliary model is maximised under the model simulated distribution, produces low bias. We also show that FII gets this low bias from its high power in rejecting misspecified models, which comes in turn from the fact that this distribution is restricted by the modelspeci ed parameters, so sharply distinguishing it from rival misspecified models.
Keywords: Moments; Indirect Inference (search for similar items in EconPapers)
JEL-codes: C12 C32 C52 (search for similar items in EconPapers)
Pages: 11 pages
Date: 2023-05
New Economics Papers: this item is included in nep-ecm
References: Add references at CitEc
Citations:
Downloads: (external link)
http://carbsecon.com/wp/E2023_15.pdf (application/pdf)
Related works:
Journal Article: Indirect Inference and Small Sample Bias — Some Recent Results (2024)
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:cdf:wpaper:2023/15
Access Statistics for this paper
More papers in Cardiff Economics Working Papers from Cardiff University, Cardiff Business School, Economics Section Contact information at EDIRC.
Bibliographic data for series maintained by Yongdeng Xu (xuy16@cardiff.ac.uk).