Aggregate Cost Stickiness in GAAP Financial Statements and Future Unemployment Rate
Bibliographic record
Abstract
ABSTRACT We examine whether aggregate cost stickiness predicts future macro-level unemployment rate. We incorporate aggregate cost stickiness into three different classes of forecasting models studied in prior literature, and demonstrate an improvement in forecasting performance for all three models. For example, when adding cost stickiness to an OLS regression that includes a battery of macroeconomic indicators and control variables, we find that a one-standard-deviation-higher cost stickiness in recent quarters is followed by a 0.23 to 0.26 percentage point lower unemployment rate in the current and following quarter. In out-of-sample tests, we find significant reductions in the root mean squared errors upon incorporation of cost stickiness for all three models. Additional tests suggest that professional macro forecasters, particularly those employed in nonfinancial industries, do not fully incorporate the information contained in cost stickiness. Finally, we find a stronger predictive power of cost stickiness toward the end of recessionary periods; we also assess cross-sectional variation of this predictive ability. JEL Classifications: M41; E24; J60.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".