The Impact of Exchange Rate Data on Canadian Inflation: An FPCA and Group LASSO Approach
Bibliographic record
Abstract
This study investigates the temporal dynamics of exchange rates between various international currencies and the Canadian dollar, with a focus on understanding how these rates influence Canadian inflation. The Functional Principal Component Analysis (FPCA) is applied to effectively reduce the dimensionality of exchange rate data and capture important modes of variation. The extracted functional principal components (FPCs) were then used in a Group LASSO regression model to identify which currencies most significantly impact inflation rates in Canada. Our analysis includes exchange rates from nine countries. The results show that the U.S. Dollar (USD), Mexican Peso (MXN), and Swedish Krona (SEK) are the most influential currencies in predicting Canadian inflation rates. By employing these advanced statistical techniques, this study provides a comprehensive assessment of how fluctuations in global currencies can affect the domestic economic environment, offering valuable insights for policymakers and financial analysts. This study contributes to the broader understanding of currency exchange impacts on inflation and highlights the importance of specific international currencies in economic forecasting.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".