May Monetary Transmission Lags Have a Role in Missing Inflation Targets in Turkey? Cointegration Tests with Structural Breaks and Structural VAR Analysis
Bibliographic record
Abstract
This paper aims at estimating the monetary transmission lag in Turkey by utilizing quarterly data from 2006:1 to 2015:4. To this end, the paper, first, follows unit root tests and cointegration tests. Then, the paper employs structural vector autoregressive (SVAR) analysis. SVAR analysis explores that a positive one-unit standard deviation shock to real interest rate causes inflation to decrease in the eighth period and the decrease in inflation prolongs up to the tenth period. Therefore, SVAR analysis yields that the monetary transmission lag is two and a half years in Turkey. Based on its own findings and those of a previously produced paper which yields that the CBRT considers 12-month ahead expected inflation rate while it is steering interest rates, the paper thus argues that monetary transmission lags may have a role in missing inflation targets in Turkey along with some other factors. In conclusion, the paper argues that the CBRT should pay attention to 24-month ahead or 30-month ahead expected inflation rates instead of 12-month ahead expected inflation rate to achieve or run-up to inflation targets.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".