Exchange Rate and the Trade Balance: Is the Link Symmetric or Asymmetric
Bibliographic record
Abstract
ABSTRACT EXCHANGE RATE CHANGES AND THE TRADE BALANCE: IS THE LINK SYMMETRIC OR ASYMETRIC By Hadiseh Fariditavana The University of Wisconsin-Milwaukee, June 2016 Under the Supervision of Professor Mohsen Bahmani-Oskooee This dissertation consists of three essays in international trade. The J-Curve theory suggests that after currency depreciation, the trade balance continues to deterioration till some lags emerge, and then starts to improve. My contribution is in using a non-linear Autoregressive Distributed Lag model to examine if the effects of depreciation are different than the effects of appreciation of exchange rate on the trade balance. Using the sample data from thirteen developed and developing countries I show that when aggregate trade data are used, the effects of those two are asymmetric. In response to changes in the real exchange rate, a country’s trade balance could improve with respect to one trade partner and could deteriorate with respect to another trade partner. Testing the J-Curve using aggregate trade data might not capture both effects at the same time. Thus in section two of chapter four, using the non-linear ARDL model, the bilateral J-Curve phenomenon between two specific trade partners is tested. I use the bilateral trade data between the United States and its sixteen major trade partners and I find support for my claim that the effects of exchange rate changes are asymmetric. Section three of chapter four takes it one step further in a way that employs the trade data of 162 trading industries between the United States and Canada to investigate the asymmetric claim. By further disaggregating bilateral trade data my results show that in majority of the cases in my sample, the effects of depreciation are significantly different than the effects of appreciation. Due to the possible positive response of one bilateral commodity flow to the exchange rate changes and possible negative response of another flow at the same time, the commodity level trade data is considered to be able to solve any possible aggregation bias of the other types of data sets.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".