Akaike-type criteria and the reliability of inference: model selection versus statistical model specification

DOI10.1016/j.jeconom.2010.01.011zbMath1431.62022OpenAlexW2047311524MaRDI QIDQ736670

Aris Spanos

Publication date: 4 August 2016

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jeconom.2010.01.011

zbMATH Keywords

GICmodel selectionleast-squaresAkaike information criterionAICBICsimplicityMDLmodel averagingmodel specificationlinear regression modelmis-specification testingstatistical adequacycurve-fittingAR(\(p\))double-use of dataGauss linear modelinfinite regress and circularitymathematical approximation theorypre-test biasreliability of inferencerespecification

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Foundations and philosophical topics in statistics (62A01) Statistical aspects of information-theoretic topics (62B10)

Related Items

Severity and Trustworthy Evidence: Foundational Problems versus Misuses of Frequentist Testing ⋮ Revisiting noncentrality-based confidence intervals, error probabilities and estimation-based effect sizes ⋮ The probabilistic reduction approach to specifying multinomial logistic regression models in health outcomes research ⋮ The econometric modelling of social preferences ⋮ Error statistical modeling and inference: where methodology meets ontology ⋮ Controlling the error probabilities of model selection information criteria using bootstrapping ⋮ Efficiency gains in least squares estimation: A new approach

Cites Work

• Modeling by shortest data description
• Estimating the dimension of a model
• Model specification: The views of Fisher and Neyman, and later developments
• On theory testing in econometrics. Modeling with nonexperimental data
• Information criteria and statistical modeling.
• Statistical predictor identification
• How to Tell When Simpler, More Unified, or LessAd HocTheories will Provide More Accurate Predictions
• Model Selection and Model Averaging
• Statistical Adequacy and the Testing of Trend Versus Difference Stationarity
• Mathematical Statistics
• Akaike Information Criterion, Curve-fitting, and the Philosophical Problem of Simplicity
• Model Selection and Multimodel Inference
• The Logic of Inductive Inference
• Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction
• Mean Square Error of Prediction as a Criterion for Selecting Variables
• Some Comments on C P
• MODEL SELECTION AND INFERENCE: FACTS AND FICTION
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item