Forecasting the Defense Budget and Assessing Its Impact on Economic Growth: An Empirical Study for Bangladesh
Bibliographic record
Abstract
This research examines the impact of critical macroeconomic variables, particularly GDP, inflation rate, and labor force participation rate, on Bangladesh’s defense budget using annual data from 1990 to 2022. The main goal is to predict future defense budgets by analyzing key economic variables that influence budget decisions. This will help understand how economic conditions shape defense budget priorities and allocations. For this research, the Autoregressive Distributed Lag (ARDL) model is used, which is characterized by a lag structure of ARDL (4,4,4,4), to study how these economic factors and the defense budget are connected in both long-term and short-term dynamics. The analysis shows a positive correlation between the defense budget and both rates of inflation and GDP growth. However, the participation rate of the labor force is negatively related to the defense budget, and short-term exposure indicates that this participation rate affects the national defense budget more immediately. The ARDL model Bounds Test approach for cointegration shows an F statistic of 12.28744, which exceeds the critical value at the 1%, 5%, and 10% significance levels, strongly suggesting a long-term correlation between the defense budget and economic factors. Furthermore, the Error Correction Model (ECM) shows that the defense budget gradually returns to a long-term equilibrium by correcting roughly 51.95% of any disequilibrium during each period. These results highlight how crucial it is for the defense budget planners in Bangladesh to take economic factors into account to ensure that the defense budget is aligned with the nation’s broader objectives for stability and economic growth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".