Common time variation of parameters in reduced-form macroeconomic models
Bibliographic record
Abstract
Abstract Standard time varying parameter (TVP) models usually assume independent stochastic processes. In this paper, I show that the number of underlying sources of parameters’ time variation is likely to be small, and provide empirical evidence for factor structure amongst TVPs of popular macroeconomic models. In order to test for the presence of low dimension sources of time variation in parameters and estimate their magnitudes, I develop the factor time varying parameter (Factor-TVP) framework and apply it to [Primiceri, G.E. (2005), “Time Varying Structural Vector Autoregressions and Monetary Policy,” The Review of Economic Studies , 72, 821–852] monetary TVP-VAR model. I find that one factor explains most of the variability in VAR coefficients, while the stochastic volatility parameters vary independently. The inclusion of post-“Great Recession” data causes an important change within VAR coefficients and the procedure suggests two factors. The roots of variability in the VAR parameters are likely to have derived from the financial markets and the real sector. The TVP factors have predictive power for a large number of output, investment, and employment series, as well as for the term structure of interest rates.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".