Foreign Direct Investment Leading Indicators: the Case Study of Thailand and Vietnam
Bibliographic record
Abstract
Background: Foreign direct investment (FDI) has played the important role in economic development, both for Thailand and Vietnam. In order to explain FDI patterns in Thailand and in Vietnam for the past 20 years, ARIMAX model is employed. The ARIMAX model, as well, is used to forecast the value of FDI in these two countries. Objective: The objective of this paper is to construct the leading indicators that are able to explain and predict the behaviors of foreign direct investment infl ow to Thailand and Vietnam. The paper introduces ARIMAX model to explain and predict the value FDI infl ow to these two countries. Data used in this paper is quarterly data during the period of 1988-2010. Result: The study f inds that GDP per capita, real interest rate, degree of openness, and exchange rate are the leading indicators for explaining and forecasting the FDI values to these two countries. Among these factors, degree of openness is the most important factor to explain the FDI pattern. Discussion and Conclusion: The study f inds that investment promotion policies and the reduction in trade transaction costs play the important role in FDI decision. The model forecasts that the value of FDI to both countries would be converge the same in the first quarter of 2012.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".