The Quantitative Importance of News Shocks in Estimated DSGE Models
Bibliographic record
Abstract
We estimate a dynamic stochastic general equilibrium (DSGE) model with several frictions and both unanticipated and news shocks, using quarterly U.S. data from 1954 to 2004 and Bayesian methods. We find that unanticipated shocks dominate news shocks in accounting for the unconditional variance of output, consumption, and investment growth, interest rate, and the relative price of investment. The unanticipated shock to the marginal efficiency of investment is the dominant shock, accounting for over 45% of the variance in output growth. News shocks account for less than 15% of the variance in output growth. Within the set of news shocks, nontechnology sources of news dominate technology news, with wage markup news shocks accounting for about 60% of the variance share of both hours and inflation. We find that in the estimated DSGE model (i) the presence of endogenous countercyclical price and wage markups due to nominal frictions substantially diminishes the importance of news shocks relative to a model without these frictions, and (ii) while there is little change in the estimated contributions of technology news when we restrict wealth effects on labor supply, the contributions of nontechnology news shocks are relatively more sensitive.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".