The Effects of Macroeconomic Variables on the Financial Stability in the Iranian Insurance Industry
Bibliographic record
Abstract
The purpose of this article is to investigate the effects of macroeconomic variables such as exchange rate, interest rate, economic growth and real money residual growth on the financial stability in the Iranian insurance industry. For this purpose, Markov switching method is used. The ability to account for changes in the relationship between macroeconomic variables and the financial stability of the insurance industry over time is one of the most important features of the Markov switching method. The period under study is from the first quarter of 2005 to the fourth quarter of 2015. The results show that the effects of macroeconomic variables during the first regime (including the first quarter of 2005 to the third quarter of 2008) and the second regime (including the fourth quarter of 2008 to the fourth quarter of 2015) on financial stability of the insurance industry are different. So that the effects of exchange rate, interest rate and economic growth on the financial stability of the insurance industry in the first regime are the opposite of those of the second regime. This is while the growth of the real balance of money has a direct link to the financial stability of the insurance industry in each round of the regime, but in the second regime, which is a recessionary regime, its effect on financial stability is insignificant. Also, the findings show that the stability of the first regime is more than the second regime, so that if the insurance industry is in regime one in the previous period, with a probability of 94% it will be again in regime one.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".