Modelo de inversi¢n basado en la ecuaci¢n de Euler con l¡mite m ximo de endeudamiento: evidencia emp¡rica internacional
Bibliographic record
Abstract
Este trabajo estudia los determinantes de la inversi¢n empresarial, incorporando un limite m ximo de endeudamiento a un modelo de inversi¢n basado en la ecuaci¢n de Euler. Las diferentes versiones del modelo desarrollado se han estimado por el M‚todo Generalizado de los Momentos para datos de Canad , Espa¤a, Estados Unidos y Reino Unido. Los resultados obtenidos muestran que la sensibilidad de la inversi¢n al cash flow es mayor para las empresas restringidas financieramente que para las no restringidas. Esta mayor sensibilidad es causada por la mayor repercusi¢n que tienen las imperfecciones del mercado en las empresas restringidas financieramente. En consecuencia, se pone de manifiesto la necesidad de considerar por separado las empresas restringidas y no restringidas para identificar los determinantes de la inversi¢n empresarial. This paper studies the determinants of firms? investment. Our model with a credit limit is derived from the Euler equation. The set of models obtained derived have been estimated by using the Generalized Method of Moments with data from Canada, Spain, The United States and The United Kingdom. Our results show that the sensitivity of investment to cash flow is greater for financially constrained firms than for those financiallyunconstrained. This greater sensitivity is caused by the higher effect of the capital market imperfections in the financially constrained firms. As a consequence, a separate study of the firm according to the financial constraints suffered is required in order to precisely identify the determinants of firms` investment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".