Explanation:
For prediction purposes, we should primarily focus on Akaike's Information Criterion (AIC) as it's specifically designed for model selection when the goal is prediction.
Analysis of criteria:
AIC: Lower values indicate better models for prediction
Adjusted R²: Higher values are better, but Model 2 (0.652) is slightly better than Model 3 (0.648)
BIC: Lower values are better, but BIC penalizes complexity more heavily and is better for finding the "true" model rather than prediction
Decision rationale:
Therefore, Model 3 should be preferred for prediction purposes.
Ultimate access to all questions.
An analyst gathers the following summary of the goodness-of-fit measures for a dependent variable regressed on alternative sets of factors:
| Independent Variables | Adjusted | Akaike's Information Criterion (AIC) | Schwarz's Bayesian Information Criterion (BIC) |
|---|---|---|---|
| Model 1: Factor 1 | 0.631 | 13.156 | 14.990 |
| Model 2: Factors 1 and 2 | 0.652 | 14.287 | 15.189 |
| Model 3: Factors 1, 2, and 3 | 0.648 | 12.463 | 16.397 |
Which of the models should be preferred for prediction purposes?
A
Model 1
B
Model 2
C
Model 3
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