Impact of Government Intervention on Inflation Control
Bibliographic record
Abstract
บทคดยอ บทความน ตรวจสอบผลกระทบการควบคมเงนเฟอจากการแทรกแซงของรฐ โดยใช เทคนคพยากรณเพอคำนวณคา fractional parameter และใหม additive inliers ท สะทอนถงนโยบายแทรกแซง การวเคราะห พจารณากรณใหมแผนกระตน ระยะสนท สำคญ ผลการศกษาชวา การแทรกแซงของรฐจะเปนผลตอการควบคมเงนเฟอเพยง ในระยะชวคราว การศกษานจงเสนอทางเลอกในการควบคมเงนเฟอ โดยนาจะพจารณาใชนโยบายเศรษฐกจทมความเหมาะสมกบสถานการณเวลาทสามารถจดการ ปญหาเงนเฟอ และขณะเดยวกนสามารถควบคมเงนเฟอไดในระยะยาว คำสำคญ : นโยบายการเงน, การควบคมเงนเฟอ Abstract This paper detects the effects of government intervention to control inflation. The long memory fractional parameter was estimated using simulation technique in the presence of additive inliers, which serve as a proxy of government intervention. The data generating process considers the case where there are shock plans, i.e. inliers that are short-lived but important in magnitude. The results show that the level to which inflation falls after the intervention has no impact on the estimate of the fractional parameter and on the persistence of the inflation process. That means any abrupt intervention would have only temporary effect on lowering the inflation rates and the series remain stationary. This implies the need for alternative measures of monetary policy. This paper recommends pursuing the time-consistency economic policy, which provides an explanation in order to combat inflation and to sustain the result for the longer period. Keywords : additive inliers, fractional parameter, Monte-Carlo simulation, inflation control
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".