How Do Inflation Rate, BI Rate, and Balance of Trade Directly Affect IDR to USD Exchange Rate and Indirectly Affect IDX Composite Index in Initial Stage of Covid-19 Outbreak?
Bibliographic record
Abstract
This study is trying to fill the research gap by understanding how several important financial variables interplay and affect the macroeconomic indicators, especially in developing country like Indonesia during the financial crisis period caused by the unexpected and sudden Covid-19 pandemic outbreak. The purpose of this research is to determine the direct effect of the inflation rate, Bank Indonesia (BI) rate, and Balance of Trade on the movement of Indonesia Rupiah (IDR) currency stability against US Dollar (USD) and indirect impact to the Indonesia Stock Exchange (IDX) Composite index in Indonesia during the initial stage of the pandemic outbreak. To achieve the research objectives, this study uses the Rupiah exchange rate against USD and IDX Composite index as the dependent variables, while the Inflation rate (CPI), BI rate, and Balance of Trade as the independent variables. The quantitative methodology is used in this research by applying linear and multiple simple regression techniques. The results of this research show that the Inflation rate, BI rate, and Balance of Trade were significantly affecting the Rupiah exchange rate against the USD, and it found that the most dominant variable in this model was the BI rate. The Inflation rate and Balance of Trade partially had no direct significant influence toward the movement of the Rupiah exchange rate against the USD during the critical anticipation period of Covid-19 pandemic outbreak. Furthermore, Rupiah exchange rate against the USD gave a significant influence toward the IDX Composite index.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".