Financial Conditions Indexes for Canada
Bibliographic record
Abstract
The authors construct three financial conditions indexes (FCIs) for Canada based on three approaches: an IS-curve-based model, generalized impulse-response functions, and factor analysis. Each approach is intended to address one or more criticisms of the monetary conditions index (MCI) and existing FCIs. To evaluate their three FCIs, the authors consider five performance criteria: the consistency of each FCI's weight with economic theory, its graphical ability to predict turning points in the business cycle, its dynamic correlation with output, its in-sample fit in explaining output, and its out-of-sample performance in forecasting output. Using monthly data, the authors find, in general, that housing prices, equity prices, and bond yield risk premiums, in addition to short- and long-term interest rates and the exchange rate, are significant in explaining output from 1981 to 2000. They also find that the FCIs outperform the Bank of Canada's MCI in many areas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 teacher head, 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".