Explaining the demand for money by non-financial corporations in the euro area: A macro and a micro view
Bibliographic record
Abstract
This paper analyses euro area non-financial corporations (NFCs) money demand, both from a macro and a microeconomic point of view. At a macro level, money holdings are modelled as a function of real gross added value, the price level, the long-term interest rate on bank lending to non-financial corporations, the own rate of return on M3 and the real capital stock of NFCs. The results indicate that NFCs money holdings adjust quickly when deviations from their long-run level are registered, and that the large increase observed recently in NFCs money holdings has been driven by changes in their fundamentals and hence they stand in line with their long-run equilibrium level. The disaggregated analysis also shows that cash holdings are linked to balance-sheet ratios (such as non-liquid short term assets, tangible assets or indebtedness) and other variables such as the firm’ cash flow, its volatility or the size of the firm, which cannot be taken into account in the macro analysis. Likewise, results indicate that the main drivers of the increase in NFCs cash holdings in the last years have been cyclical factors, captured by gross-added value and the cash-flow respectively. Variations in the opportunity cost of holding money, have also contributed to explain M3 developments but more modestly than at the end of the nineties, when its increase contributed negatively to cash accumulation
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".