Parametric Bootstrap Model Selection Criterion With in Linear Model Compared To Other Criteria

dc.contributor.author Ucal, Meltem Şengün
dc.contributor.author Ucal, Meltem
dc.contributor.other Economics
dc.date.accessioned 2019-06-27T08:00:53Z
dc.date.available 2019-06-27T08:00:53Z
dc.date.issued 2005
dc.department Fakülteler, İktisadi, İdari ve Sosyal Bilimler Fakültesi, Ekonomi Bölümü en_US
dc.description.abstract The most important stage of econometrics estimation is the in model set up. The model set up has the best prediction ability and is therefore suitable for econometrics estimation. Between the dependent variable Y and other independent explanatory variables X must be a strong relationship in econometrics estimation. However all explanatory variables cannot be related to dependent variable Y. This condition creates a regression problem. A similar problem appears in variable selection equivalent to problem in model selection. The most suitable faultless model is provided by correct and suitable selection of variables. There exist many variable/model selection procedures the where the necessary relationship between X and Y is linear en_US]
dc.description.abstract for example the C-P method en_US]
dc.description.abstract the Bayes Information Criterion (BIC) (Hannan-Quin) en_US]
dc.description.abstract the Final Prediction Error Method (FPE lambda - Shibata. 1984) en_US]
dc.description.abstract Akaike Information Criterion (ACI) en_US]
dc.description.abstract Schwartz Criterion (SC) en_US]
dc.description.abstract Cross-Validation (CV) en_US]
dc.description.abstract Generalized Cross Validation (GCV) (Craven-Wahba) en_US]
dc.description.abstract Log Likelihood en_US]
dc.description.abstract Bootstrap and Jackknife. In this paper we compare some different model selection criteria with the parametric bootstrap and present a simple procedure to obtain a linear approximation of the mean squared prediction error. This study is based on empirical evidence and model training. en_US]
dc.identifier.citationcount 0
dc.identifier.endpage 357
dc.identifier.isbn 978-980-6560-60-4
dc.identifier.scopus 2-s2.0-84867374925 en_US
dc.identifier.startpage 354 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/141
dc.identifier.wos WOS:000243687600068 en_US
dc.institutionauthor Ucal, Meltem Şengün en_US
dc.language.iso en en_US
dc.publisher INT INST Informatics & Systemics en_US
dc.relation.journal WMSCI 2005: 9th World Multi-Conference on Systemics, Cybernetics and Informatics, Vol 8 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 0
dc.subject Model selection criteria en_US
dc.subject Bootstrap selection criterion en_US
dc.subject Candidate for true model en_US
dc.subject Mean squared prediction error en_US
dc.subject Bootstrap estimator of the expected excess error en_US
dc.title Parametric Bootstrap Model Selection Criterion With in Linear Model Compared To Other Criteria en_US
dc.type Conference Object en_US
dc.wos.citedbyCount 0
dspace.entity.type Publication
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