Identifying the Temporal Dynamics and Macroeconomic Interactions of the US Economy
Bibliographic record
Abstract
This paper employs the VAR model to analyze interrelations among key macroeconomic variables, emphasizing unemployment, inflation, and the Fed Funds rate. The model reveals asymmetry in the unemployment-Fed Funds rate relationship, emphasizing the unique influence of unemployment. Lagged values contribute to understanding temporal dependencies, highlighting positive associations between lagged and current inflation. Impulse response analysis and the covariance matrix validate the IS-LM model and Stock and Watson’s (2001) findings. Forecasts anticipate increased unemployment and a slight Fed Funds rate decrease, though accuracy tests reveal reliability issues, especially for the Fed Funds rate. ADF tests support stationarity for inflation and unemployment showing a weak indication against the unit root hypothesis for the Fed Funds rate. Lastly, SARIMA, ARIMA, and DM tests suggest performance differences, pointing to avenues for future research to enhance precision, address reliability issues, and explore variations between SARIMA and VAR models, potentially in a cross-country comparative context.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".