Business Cycles, Investment Shocks, and the "Barro-King" Curse
Bibliographic record
Abstract
Recent empirical evidence identifies investment shocks as key driving forces behind business cycle fluctuations.However, existing New Keynesian models emphasizing these shocks counterfactually imply a negative unconditional correlation between consumption growth and investment growth, a weak positive unconditional correlation between consumption growth and output growth and anomalous profiles of cross-correlations involving consumption growth.These anomalies arise because of a short-run contractionary effect a positive investment shock on consumption.Such counterfactual co-movements are typical of the "Barro-King curse" (Barro and King 1984), wherein models with a real business cycle core must rely on technology shocks to account for the observed co-movement among output, consumption, investment, and hours.We show that two realistic additions to an otherwise standard medium scale New Keynesian model -namely, roundabout production and real per capita output growth stemming from trend growth in neutral and investment-specific technologies -can break the Barro-King curse and provide a more accurate account of unconditional business cycle comovements more generally.These two features substantially magnify the effects of neutral technology and investment shocks on aggregate fluctuations and generate a rise of consumption on impact of a positive investment shock.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".