Interest Rate Pass-Through to Macroeconomic Variables: The Nigerian Experience
Bibliographic record
Abstract
The effectiveness of monetary policy depends on the adjustment response of Central Banks short-term interest rate on the real interest rates charged by commercial banks and ultimately on macroeconomic indicators of investment and consumption in the economy. Thus, the extent of interest rate pass-through largely depends on how effective the process of financial intermediation works and to what extent individual bank characteristics influence or hinder a perfect adjustment of product rates based on market conditions. The study examines the speed and completeness of pass-through from policy rates to retail bank rates and the effectiveness of monetary policy stance in influencing macroeconomic policy targets using a co-integration analysis based on Johansen and Juselius maximum likelihood and Engle-Granger two step procedures for the period 1970–2011. The VAR based Error Correction Model (ECM) and the Mean Adjustment Lag (MAL) was used to determine the short run estimates and asymmetric behaviour respectively. The study found an evidence of downward stickiness both in the short-run and long-run policy pass-through to the retail bank rates. In order to ensure robustness of the result, the Impulse Response Function (IRF) and Variance Decomposition (VD) analysis were conducted and similar slow and sluggish pass-through was obtained. The study as well, found pass-through from policy rate to macroeconomic variables to exhibit extremely rigid immediate responses.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".