DOES THE RELITIVE PRICE OF NON-TRADED GOODS CONTRIBUTE TO THE SHORT-TERM VOLATILITY IN THE U.S./CANADA REAL EXCHANGE RATE? A STOCHASTIC COEFFICIENT ESTIMATION APPROACH by Terrill D. Thorne
Bibliographic record
Abstract
This study uses a random coefficient estimation procedure to test the hypothesis that much of the volatility in the U.S./Canada real exchange rate over the time period 1971 through 1999 is due to the relative price of non-traded goods to traded goods. The model specification used in this study provides estimates of the sensitivity of movements in the U.S./Canada real exchange rate to movements in both the relative price of traded goods and the relative price of non-traded goods to traded goods in each of the two countries. I test for purchasing power parity in each of the two components of the model and address the question of volatility through the examination of the time profile of the respective coefficient estimates. The empirical results support the conclusion that the average value of the coefficient on the relative price of nontraded goods to traded goods component is smaller than that on the relative price of traded goods component. However, purchasing power parity in both components can not be rejected when the period of study is limited to 1971 through 1994. Furthermore, examination of the time profile of the random coefficients on the relative price of non-traded goods to traded goods component suggests that it is much more volatile and, therefore, quite significant in capturing the volatility in U.S./Canada real exchange rate movements. With regard to purchasing power parity in both the traded goods component and the nontraded goods to traded goods component, these results are consistent with the implications of the theory of purchasing power parity. However, they are not entirely consistent with the evidence presented in recent literature. Specifically, evidence presented in recent studies can not support perfect purchasing power parity in either traded g...
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".