A Simple Stock-Flow Consistent Model with Short-Term and Long-Term Debt: A Comment on Claudio Sardoni
Bibliographic record
Abstract
In a recent article of this journal, Claudio Sardoni ([2019]. ‘Investment and Saving in a Dynamic Context: The Contribution of Athanasios (Tom) Asimakopulos.’ Review of Political Economy 31 (2): 233–246) made four claims: (1) An increase in the propensity to save will lower the long-term interest rate; (2) A higher preference for bonds will lead to lower long-term interest rates; (3) A higher level of investment will lead to a higher long-term interest rate; (4) A larger (exogenous) supply of money will lead to a lower long-term interest rate. We confront these four claims with the help of a simple stock-flow consistent (SFC) model which includes firms, banks and households, with the latter holding either bank deposits or bonds issued by firms, while these firms invest in fixed capital and in inventories. We find that higher investment leads to higher interest rates on bonds in the short run, but not in the medium or long run. Similarly, a higher desired inventories-to-output ratio ends up leading to lower interest rates both in the short and the long run. We conclude that using SFC models is particularly adequate when dealing with issues that integrate real and financial variables.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".