THE EFFECT OF DEFENSE SPENDING ON US OUTPUT: A FACTOR AUGMENTED VECTOR AUTOREGRESSION (FAVAR) APPROACH
Bibliographic record
Abstract
Empirical evidence on the effect of defense spending on US output is at best mixed. Against this backdrop, this paper assesses the impact of a positive defense spending shock on the growth rate of real GNP using a Factor Augmented Vector Autoregressive (FAVAR) model estimated with 116 variables spanning the quarterly period of 1976:01 to 2005:02. Overall, the results show that a positive shock to the growth rate of the real defense spending translates to a positive short‐run effect on the growth rate of real GNP lasting up to ten quarters, but the effect is significant only for two quarters. Beyond the tenth quarter, the effect becomes negative and shows signs of slow reversal at around the 17th quarter. Our results tend to indicate that the mixed empirical evidence, based on small‐scale Vector Autoregressive (VAR) and Vector Error Correction (VEC) models, could be a result of a small information set not capturing the true theoretical relationships between the two variables of interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".