A dynamic theory of the Feldstein-Horioka puzzle and financial frictions: Re-estimation of the saving retention coefficient
Bibliographic record
Abstract
Many empirical studies of the Feldstein-Horioka Puzzle (FHP) conducted so far have common drawbacks; among others, those estimates are free from economic models, and their specification errors are not avoidable.In other words, statistically significant estimates of the saving retention coefficient are not known yet.Taking a complete different approach from earlier studies to elucidate FHP, I re-estimate the savings retention coefficient (called "beta") indirectly based on the perfect-foresight saddle-path dynamics of investment under convex adjustment cost.I assume that the error term of the regression model will represent various shocks and effect of financial frictions.Our main empirical results are; (1) Replication of the FHP in the 16 OECD countries during 1964-1974 yields 0.52 as the estimate of the beta, which is much smaller than the estimate of Feldstein and Horioka (1980).(2) Estimates using samples for every 10 years during 1960-1999 exhibit that the beta decreases gradually.(3) During 2000-2008, the FHP temporarily disappeared and then the higher beta returned after 2008.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".