Forecasting Canadian Inflation Rate Using CPI and Interest Rate Data: A Time Series Analysis
Bibliographic record
Abstract
Forecasting short-term inflation in Canada requires accounting for both price trends and market volatility. To address this challenge, this study applies a time series model that combines monthly Consumer Price Index (CPI) data with the 3-month interbank interest rate – a signal often associated with monetary policy adjustments. The model specification, ARIMA(0,1,2) coupled with a GARCH(1,1) process, is estimated under a t-distribution to accommodate heavy-tailed residuals. With monthly observations from January 2002 to August 2024 for training the model and withholding the following seven months as a track record, the analysis finds that CPI changes have a mild but stable positive trend. Moreover, interest rate appears as a significant factor, which increases its predictive power in inflation-linked models. The approach improves short-run accuracy, while retaining economic feasibility. This design could have useful application for policymakers who try to predict pressure in prices. Extensible cases include the model of nonlinear feedback or the inclusion of external drivers – energy shocks and global financial volatility.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".