A model selection method for S-estimation
Arie Preminger and
Shinichi Sakata ()
Econometrics Journal, 2007, vol. 10, issue 2, 294-319
Cleaning data or removing some data periods in least squares (LS) regression analysis is not unusual. This practice indicates that a researcher sometimes desires to estimate the parameter value, with which the regression function fits a large fraction of individuals or events in the population (behind the original data set), possibly exhibiting poor fits to some atypical individuals or events. The S-estimators are a class of estimators that are consistent with the researcher's desire in such situations. In this paper, we propose a method of model selection suitable in the S-estimation. The proposed method chooses a model that minimizes a criterion named the penalised S-scale criterion (PSC), which is decreasing in the sample S-scale of fitted residuals and increasing in the number of parameters. We study the large sample behavior of the PSC in nonlinear regression with dependent, heterogeneous data, to establish sets of conditions sufficient for the PSC to consistently select the best-fitting, most parsimonious model. Our analysis allows for partial unidentifiability, which is an important possibility when selecting one among non-linear regression models. We conduct Monte Carlo simulations to verify that a particular PSC called the PSC-S is at least as trustworthy as the Schwarz information criterion, often used in the LS regression. Copyright Royal Economic Society 2007
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://www.blackwell-synergy.com/doi/abs/10.1111/j.1368-423X.2007.00209.x link to full text (text/html)
Access to full text is restricted to subscribers.
Working Paper: A model selection method for S-estimation (2005)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ect:emjrnl:v:10:y:2007:i:2:p:294-319
Ordering information: This journal article can be ordered from
Access Statistics for this article
Econometrics Journal is currently edited by Richard J. Smith, Oliver Linton, Pierre Perron, Jaap Abbring and Marius Ooms
More articles in Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing ().