A study of the relationship between non-performing loan ratio of domestic banks and business cycle
Bibliographic record
Abstract
[[abstract]]民國86年（1997年）7月發生亞洲金融風暴以來，國內景氣轉現下降趨勢，至民國88年（1999年）第一季後景氣始逐漸回升，爾後歷經波動，直至民國98年（2009年）第一季止，依據經建會所公布之資料，台灣共出現三次明顯的景氣循環，而期間也因銀行逾放比率的影響促使政府推動金融改革。因此，本國銀行逾放比率與景氣循環之關聯性成為本研究所欲探討之重點。 本研究以本國銀行逾放比率為依變數，並分別以台灣景氣指標中的景氣對策信號綜合分數與景氣動向指標構成項目為自變數，建立兩種模型，時間範圍為民國88年（1999年）至民國99年（2010年），運用迴歸分析法檢視本國銀行逾放比率與景氣指標因子之間的關聯性。實證結果發現，(1)本國銀行逾放比率與景氣對策信號綜合分數之間，呈現負向關係。(2)在複迴歸模型中，貨幣總計數M1B、核發建造面積、非農業部門就業人數與實質製造業銷售值四項指標，與本國銀行逾放比率的變動較為相關，其中以核發建造面積與非農業部門就業人數兩項指標最為顯著。 Since the Asian financial crisis of July 1997, the domestic economy worse off, to the first quarter in 1999 the economy beginning gradually recovered. After the twists and turns, until the first quarter in 2009, according to data published by the CEPD, there were three distinct Taiwanese business cycles. During this period, but also because of the non-performing loan ratio of banks to improve the impact, prompting the government for financial reform. Therefore, this study will explore the relationship between the domestic bank's NPL ratio and the business cycle to identify the factors influenced business cycle. In this study, the non-performing loan ratio of domestic banks act as dependent variables, and then, using two kinds of taiwanese business indicators as independent variables, the monitoring indicators (total score) and the component series. We could establish two models. The object of 1999-2010, by using regression analysis to examine the relationship between the domestic bank's NPL ratio and the business indicators. The conclusions includes two parts: (1) there were negative linear regression relationship between the domestic bank's NPL ratio and the monitoring indicators, (2) four factors of the multiple regression model, they were monetary aggregates M1B, building permits, nonagricultural employment and index of producer's shipment for manufacturing, associated with changes in the domestic bank's NPL ratio. And two factors of them, which building permits and the nonagricultural employment, could reach significant levels.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".