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Related Experiment Videos

Statistical Tests for Comparing Possibly Misspecified and Nonnested Models.

Golden1

  • 1University of Texas at Dallas

Journal of Mathematical Psychology
|March 29, 2000
PubMed
Summary
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A new Model Selection Test (MST) allows researchers to determine if one model

Area of Science:

  • Econometrics
  • Statistical Modeling
  • Model Selection

Background:

  • Model selection criteria (MSC) are essential for choosing the best-fitting model to data.
  • Existing methods may lack robustness in certain scenarios, such as model misspecification or nonnested models.

Purpose of the Study:

  • Introduce a large-sample Model Selection Test (MST) to compare goodness-of-fit between models.
  • Provide a method compatible with existing Model Selection Criteria (MSC) procedures.

Main Methods:

  • Develop the Model Selection Test (MST) based on Vuong's (1989) methodology.
  • Extend the generalized likelihood ratio test for broader applicability.
  • Validate the MST in the presence of model misspecification and for nonnested probability models.

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Main Results:

  • The MST provides a statistically sound method for differentiating between model fits.
  • Simulation studies demonstrate the MST's effectiveness and its conservative decision rule compared to MSC.

Conclusions:

  • The Model Selection Test (MST) offers a valuable extension to existing model selection techniques.
  • It enhances the ability to rigorously compare and select between statistical models, even under challenging conditions.