Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The rise in US partisan conflict following the Great Recession led to a popular belief that uncertainty about fiscal policy was impeding output growth. I explore this hypothesis by nesting it in a standard structural vector autoregression (SVAR) model traditionally used for estimating fiscal multipliers. I augment the model with stochastic volatility (a measure of uncertainty) and allow that to interact with the endogenous variables. I consider various trend assumptions, subsamples and information sets and find that the evidence does not support this hypothesis. The results reveal that there is no systematic relationship between fiscal policy uncertainty and output. Moreover, a time-varying parameter version of the model shows that the lack of consistency across specifications is not driven by changes in the transmission of uncertainty shocks over time. Finally, I revisit Fernández-Villaverde, Guerrón-Quintana, Kuester, and Rubio-Ramírez (Fernández-Villaverde, J., P. Guerrón-Quintana, K. Kuester, and J. Rubio-Ramírez. 2015. “Fiscal Volatility Shocks and Economic Activity.” American Economic Review 105: 3352–3384) who find a significant negative relationship between fiscal policy uncertainty and output. I show that when their estimation is modified to incorporate the uncertainty around the estimate of uncertainty, their empirical result falls in line with the findings in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it