Measuring the Effect of Covid-19 on Bank Lending: Empirical Evidence from Albania
Bibliographic record
Abstract
This study aims to empirically contribute to the identification and evaluation of microeconomic and macroeconomic indicators at the level of lending in Albania. It identifies a number of important factors, such as the level of gross domestic product, return on assets, unemployment rate, inflation rate, non-performing loans rate, capital adequacy, liabilities and regulatory capital to assets risk weighted. Quantitative analysis and econometric models will study the quantitative impact of each of these factors on both the level of net credit stock and the level of new credit. The creation of these 2 econometric models will serve us to measure and evaluate the changes encountered in the dependent variable over a given period of time, as a result of shocks from other variables. Also, a current and important contribution to this thesis relates to the impact assessment of COVID-19. In order to maintain the simplicity and usefulness of the model, some realistic features of the current economy have been left out, such as the level of loan repayment etc. The study period is from the first quarter of 2009 to the fourth quarter of 2020. The data used were obtained from the Bank of Albania and the Albanian Association of Banks, which were presented in the form of a time series. Despite the limited number of data considered regarding the impact of COVID-19 as well as their temporal distribution, this study with the work it performs, serves as a good starting point for further studies in this field.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".