A Comparison of the Akaike and Schwarz Criteria for Selecting Model Order

Koehler, Anne; Murphree, Emily S.

doi:10.2307/2347338

Cited by 139 publications

(93 citation statements)

References 18 publications

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“…It is interesting to note that the best fitting model is not unambiguously from using AIC or SIC. This is a common problem that has been discussed in other studies such as Koehler and Murphree (1988). The only instance when AIC does not improve upon the base models is in the case of lung cancer, which suggests the use of 34 groups, which is unusually high based on the other selections.…”

Section: Model Selectionmentioning

confidence: 94%

“…Both AIC and SIC provide a basis upon which to select from nested models by minimizing the associated criteria. However, Koehler and Murphree (1988) point out that in their analysis the two criteria indicate different models 27% of the time. Their research suggests that SIC is often preferred due to the improved predication accuracy from SIC-preferred models.…”

Section: Weighted Tobit Regression Modelmentioning

confidence: 96%

See 1 more Smart Citation

The Health Care Access Index as a Determinant of Delayed Cancer Detection Through Principal Component Analysis

Belasco¹,

Philips²,

Gong³

2012

Principal Component Analysis - Multidisciplinary Applications

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Section: Model Selectionmentioning

confidence: 94%

Section: Weighted Tobit Regression Modelmentioning

confidence: 96%

The Health Care Access Index as a Determinant of Delayed Cancer Detection Through Principal Component Analysis

Belasco¹,

Philips²,

Gong³

2012

Principal Component Analysis - Multidisciplinary Applications

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“…Additionally, optimal lag period selection is important, with the two appropriate rules of the Akaike Information Criterion (AIC) and Schwartz information criterion (SIC) rules. As per Koehler and Murphree (1988), AIC is only a convenient construction loosely derived from maximum likelihood and has negative outcome, the SIC is strongly connected to the Bayesian theory. Therefore, this study uses the SIC rule to determine the optimum lagged term as follows.…”

mentioning

confidence: 99%

Elucidating the Relationship among EUA Spot Price, Brent Oil Price and Three European Stock Indices

Hu¹,

Hu²,

Chien³

2016

ujaf

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For decades, humans have been consuming large quantities of oil, coal and natural gas. Consequently, people must now take responsibility for having participated in productive activities that have caused the emissions of greenhouse gas (GHG) which has damaged the environment and caused problems associated with abnormal weather. Previous studies investigated the relationships between energy and carbon prices, between oil price and stock index, or between carbon price and macro-economic factors. Few have examined the relationships among EUA spot price, oil price and the stock index in individual nations. Owing to the fact that the European Emissions Trading Scheme (EU ETS) is the world's first carbon market and remains the largest globally, this study, based on the finding of Chevallier (2009) that capital markets are closely related to the commodity markets, examines the long-term equilibrium relationship and causality among European Union Allowance (EUA) spot price, Brent oil price and three European major stock indices from January 1, 2005 to Dec. 31, 2012. The sample period is further divided into three sub-periods of 2005 to 2007 (Phase 1 of the EU ETS), 2008 to 2010 (US subprime loan crisis and the first period of Phase 2 of the EU ETS), and 2011 to 2012 (European debt crisis and the second period of Phase 2 of the EU ETS). Numerous notable findings from the empirical findings are presented. First, EUA spot price, oil price and DAX index are co-integrated with each other during the second sub-period. Although oil price can be adjusted to the long-term equilibrium in German stock market during that period, adjusting EUA spot price to long-term equilibrium is rather difficult. Next, oil price is affected by EUA spot price unilaterally for the full sample period and the third sub-period. Moreover, EUA spot price is unaffected by any factor except itself during the first sub-period, and is affected by three European stock indices for the full sample period, and the third sub-period. Furthermore, the most explanatory power for Brent oil and EUA spot prices arises from themselves, respectively. Finally, the capital markets and commodity markets are closely related during the 2 nd sub-period only.

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“…Cependant, quelques auteurs, Hannan et Quinn (1979) par exemple, pour les modèles AR, ont étudié les performances des critères dans des cas où il n'existe pas de vrai ordre fi ni. Des études de simulation de ce type sont Hannan et Quinn (1979), Geweke et Meese (1981), Lütkepohl (1985), Koehler et Murphree (1988), Mills et Prasad (1992), Ducharme (1997) et Anderson et al (1998). Comme le présent article, toutes ces études traitent du problème de l'estimation de la meilleure représentation d'un processus et non pas de l'estimation d'une caractérisation d'un processus de nuisance à utiliser dans des tests d'hypothèses.…”

Section: éValuation De Critères D'information Pour La Sélection De Mounclassified

Évaluation de critères d’information pour les modèles de séries chronologiques

Galbraith¹,

Zinde‐Walsh²

2005

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Résumé Il existe plusieurs critères d’information dont le but est de faciliter la sélection du modèle statistique représentant le mieux possible la réalité. Ces critères s’appliquent notamment au cas des modèles de séries chronologiques à une seule variable. La théorie asymptotique peut être utilisée pour faire un choix entre ces critères. Par exemple, si le modèle possède un ordre authentique, il peut être démontré que certains critères sont fortement convergents pour cet ordre. Historiquement, l’estimation en échantillon fini se base sur la sélection d’un ordre unique, même si plusieurs auteurs reconnaissent l’importance du cas où il n’existe pas de vrai ordre fini. Nous proposons ici un survol de la littérature sur les critères d’information et sur leur comparaison asymptotique et en échantillons finis. Nous présentons également quelques comparaisons de critères en échantillons finis en ne prenant pas pour acquis un ordre authentique au modèle. Nous utilisons alors une mesure de distance dans le but d’évaluer les performances de divers critères dans la sélection de modèles simulés. Cette mesure nous permet de juger l’exactitude de la sélection de l’ordre des modèles résultant de l’utilisation des critères (la sélection non optimale) par rapport à la sélection de l’ordre des modèles simulés (la sélection optimale). Ceci n’est pas possible dans le cas où l’on assume une forme vraie par rapport à laquelle on compare notre modèle.

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A Comparison of the Akaike and Schwarz Criteria for Selecting Model Order

Cited by 139 publications

References 18 publications

The Health Care Access Index as a Determinant of Delayed Cancer Detection Through Principal Component Analysis

The Health Care Access Index as a Determinant of Delayed Cancer Detection Through Principal Component Analysis

Elucidating the Relationship among EUA Spot Price, Brent Oil Price and Three European Stock Indices

Évaluation de critères d’information pour les modèles de séries chronologiques

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