Lecture 5: Aggregate fluctuations
Bibliographic record
Abstract
Before we can talk about the features of the business cycle, we have to define what it is. The first thing we need to do is to remove the trend. There are several distinct ways of doing that. The first is to take logs and look at differences, in other words to look at growth rates. This leads to very short cycles. Another is to take logs and remove a linear trend, in other words to look at percentage deviations from a geometric trend. This leads to very long cycles. A third, more flexible option is the Hodrick and Prescott (1980) filter. For simplicity, let’s stick with the percentage deviation from a geometric trend (or just the deviation of the log from a linear trend). Then the following facts stand out: 1. Consumption and investment and hours are positively correlated with output. 2. Consumption is less volatile than output 3. Investment is much more volatile than output 4. Hours worked are about as volatile as output 5. Output per hour worked is positively correlated with output itself. 6. Hours worked are uncorrelated with output per hour worked. If you want to verify these facts, and explore others, have a look at Canadian macro data.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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; both teacher heads agree on what is shown here.
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".