The role of real and nominal variables in defining business cycles: dynamic properties of a hybrid model - an alternative view
Bibliographic record
Abstract
The paper provides an alternative view to the Real and New Keynesian business cycle theories. The paper focuses on the combination of both real and nominal variables in explaining the cyclical movements of business cycles. We propose using Vector Autoregressive (VAR) technique on the production function approach in order to empirically assess the relative importance of both real and nominal variables in defining the shape of a business cycle (or output gap). An economy-specific variable (inflation) is introduced in the production function and is used to control the severity, persistence and magnitude of a given real shock. The model employed is tested in four countries namely: United States of America, United Kingdom, Canada and Germany. The results show that indeed real and nominal variables play an important and major role in explaining movements in business fluctuations. The bulk of impulse responses given a real shock to the output gap may also be attributed to movements in nominal variables mainly as a result of inflationary movements. This economy specific parameter conveys the same message that Ragnar Frisch hypothesized in 1933 based on his ‘rocking-horse theory’. The paper thus provides policy makers to identify key choice variables to use when reducing the impact of shocks in a given economy within a specified period of time.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".