Military expenditure forecasting methods (Economics of European defense industry)
Bibliographic record
Abstract
This dissertation firstly examines literature connected to this topic in chapter 2. Secondly, chapter 3 summarizes necessary methodology and data used throughout the dissertation. Thirdly, it compares the results of military expenditure made by Cobb-Douglas-Solow production function forecast and an Auto regressive model, in chapter 4. Fourthly, in the chapter 5, with a better performing model, it forecasters the military expenditure from 2015 to 2024 for France, Germany, UK and Italy, because they represent more than 65% of European military expenditure and should give us an idea about the course of the European expenditure as a whole. Also, it compares forecasted expenditure of European NATO countries and USA with Canada for the same period. Finally, in chapter 6, we examine whether there is Granger causality between MS and GDP. Simply, if MS Granger causes GDP and vice versa. It was found that AR is a better performing forecasting technique than CDS and that Granger causality results are ambiguous. GDP Granger causes MS only for France and Italy and there is no evidence of opposite causality.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".