The Impact of Exchange Rate Volatility on Foreign Direct Investment
Bibliographic record
Abstract
This study investigates the impact of exchange rate volatility on the level of foreign direct\ninvestment inflows in the Unites States of America, the United Kingdom, Canada and Japan, using\nannual data starting from 1975 to 2011. Exchange rate volatility has been measured using four\ndifferent methods: a classic standard deviation, a moving mean difference value of the exchange\nrate fluctuations, a moving average standard deviation with a 3-year window and, finally, using a\nGARCH(1,1) model. The data was accounted for serial correlation, nonstationarity and cointegration\nand the relationship between inward FDI flows (expressed as a percentage of GDP for each country)\nand exchange rate volatility has been analyzed using OLS regressions and a panel data model, as well\nas an error correction model to investigate the existence of a short-term relationship between the\ntwo variables. While OLS estimates have shown that FDI inflows in three out of the four countries\nanalyzed are influenced by exchange rate volatility, no evident link between the two variables has\nbeen found in the panel data analysis. In general, the mixed results obtained are proof that the\nexistence of a relationship between FDI inflows and exchange rate volatility varies across countries\nand between different econometric models employed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".