ANALISIS PENGARUH VARIABEL MAKROEKONOMI DAN COUNTRY RISK TERHADAP INVESTASI PORTOFOLIO ASING DI INDONESIA
Bibliographic record
Abstract
The reseach aims to explain the effect of country risk and variabels macroeconomics to the foreign portofolio invesment in Indonesia in short term and long term. The analysis takes time series time series data from 2006 quarter 1 through 2016 quarter 4by using Error Correction Model (ECM). The source of data are Badan Pusat Statistik, Bank Indonesia, FX Sauder and World Bank. The result are in the short term the exchange rate and economic growth effect the shock that will influence the foreign portofolio invesment. In the long trem the inflation, interst rate, money supply and country risk influence on foreign portofolio invesment significanly. The suggestion in this research is, the goverment sould keep the stability balance of payment in Indonesia .Any change, the condition of balance of payments effect appreciation and depreciation to Rupiah. To increase the economic growth in Indonesia, goverment could increasing the fiscal income and PMDN realization that will increase the enterprises productivity.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".