Investigating Government Spending Multiplier for the US Economy: Empirical Evidence Using a Triple Lasso Approach
Bibliographic record
Abstract
An essential dilemma in economics that has yielded ambiguous answers is whether governments should spend more in recessions.This paper provides an extension of the work of Ramey & Zubairy (2018) for the US economy according to which the government spending multipliers are below unity, especially when the economy experiences severe slack.Nonetheless, their work suffered from some limitations with respect to invertibility and weak instrument problem.The contribution of this paper is twofold: Firstly, it provides evidence that a triple lasso approach for the lag selection is a useful tool in removing the invertibility issues and the weak instrument problem.Secondly, the main results using a triple lasso approach suggest multipliers below unity for most cases with no evidence for differences between different states of the economy.Nevertheless, re-running the code in Ramey & Zubairy (2018), the case where WWII is excluded exhibits multipliers above unity, in both the military news and Blanchard-Perotti specifications, contradicting their baseline findings and providing evidence for a more effective government spending in recessions.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".