Bibliographic record
Abstract
The objective of this paper is to examine possible cyclical patterns in the lending behavior of Estonian commercial banks. Furthermore, the degree of cycle synchronization between the business cycle and the credit cycle and how much one cycle is behind the other in the case of Estonia is of particular interest. The paper uses data from between January 1994 and February 2004 to identify the Estonian business and credit cycles and compare their features. A Markov regime-switching technique was used in dating both business and credit cycles. Variables of interest in terms of the credit cycle include the total amount of loans provided by commercial banks, household loans, corporate loans, and the share of overdue loans in the total portfolio. Both, monthly and quarterly data was utilized to double-check the results and the business cycle was dated using the Industrial Production Index (IPI) and GDP, respectively. The share of overdue loans in the total portfolio appeared to be counter-cyclical as expected. Changes in the IPI seemed to cause changes in corporate loans with a two-quarter lag on average. Asymmetries between the credit and business cycles were found for Estonia. That is, it takes approximately five months from the beginning of an economic slowdown before corporate loans move into a contraction regime. However, it takes approximately eight months for corporate loans to recover their expansionary growth rate. Changes in household loans seemed to precede changes in economic activity by approximately one quarter.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".