The Dynamic Relationship between Real Income, Price Ratio, Exchange Rate, and International Trade-Evidence in Taiwan's Trade to US and Japan
Bibliographic record
Abstract
[[abstract]]本研究主要係以台灣對美國與日本貿易為例，探討實質所得、相對價格和匯率與國際貿易之間的動態關聯，採用的模型有 ADF、VAR和動態貿易模型，研究樣本期間為 1990年 1季至 2010年 4季之季資料。 根據研究結果顯示，就台美貿易而言，美國實質所得領先台灣對美國出口值之時差為 5季，相對價格領先台灣對美國出口值 6季，台美匯率領先台灣對美出口值 6季。再就台日貿易來看，台灣實質所得領先台灣對日進口值 5季，相對價格領先台灣對日進口值 3季，台日匯率領先台灣對日進口值 3季。再根據貿易動態模型分析結果指出，在台灣對美國出口中，相對價格與台灣對美國出口值呈現負向顯著關係，匯率與台灣對美國出口值呈現正向不顯著關係，美國實質所得與台灣對美國出口值呈現正向顯著關係。在台灣對日本進口方面，相對價格與台灣對日本進口值呈現負向不顯著關係，匯率與台灣對日本進口值呈現正向顯著關係，台灣實質所得與台灣對日本進口值呈現正向顯著關係。 The main purpose of this study is to explore the dynamic relationship between real income, price ratio, exchange rate and International trade in Taiwan. The Vector autoregression method and the dynamic method are applied to study the relationship between international trade and its determinants. The data applied are quarterly ranging from the first quarter of 1990 to fourth quarter 2010. The vector autoregressive model results indicate that U.S. real income lag before Taiwan's exports to five quarters, price ratio and exchange rate lag before Taiwan's exports to six quarters. Other results indicate that Taiwan’s real income lag before Taiwan's imports to five quarters, price ratio and exchange rate lag before Taiwan's imports to three quarters. The export equation results indicate a significant negative effect of the price ratio on Taiwan's exports and a significant positive effect of the U.S. real income on Taiwan's exports. The import equation results reveal a significant positive effect of the Taiwan’s real income and exchange rate on Taiwan’s imports.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".