The relationship between monetary policy rate decision, inflation and unemployment in the euro area
Bibliographic record
Abstract
This study examines the relationship between the interest rate on main refinancing operations which is one of the ECB’s key rates and inflation, and also the key rate and unemployment in the euro area from the quarterly data from the first quarter of 2009 to the first quarter of 2021 by Granger causality test. A pre-condition for a Granger Causality test is that the data is stationary, so we first test whether our sample data is stationary by the Augmented Dickey-Fuller (ADF) test with Schwarz Information Criterion (SIC) which also known as Bayesian Information Criterion (BIC). As the main refinancing operation rate, the Harmonised Index of Consumer Prices (HICP) inflation and the unemployment rate data of the eurozone is non-stationary, we use the first-order difference method to make the data stationary. Then, we check again whether the data at first difference is stationary by the ADF test with the SIC. The results show that the data become stationary. Therefore, the Granger causality test is adopted to investigate the relationship with these variables. The findings show that the past 8-lag value of the HICP inflation can be used to predict the monetary policy rate decision represented by the interest rate on main refinancing operations and the past 10-, 11- and 12-lag value of the unemployment rate is useful to forecast the rate on main refinancing operations while the past 10-lag value of the unemployment has the strongest prediction power. Conversely, a change in main refinancing operation rate does not improve the inflation and the unemployment rate.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".