Threshold Regression Model for Taylor Rule: The Case of Turkey
Bibliographic record
Abstract
This paper employs the structural threshold approach of Kourtellos et al. (2016) to examine various specifications of the Taylor rule model. Contrary to the previous work on the Taylor rule, this methodology allows for endogeneity of the threshold variable in addition to the right-hand-side variables suggesting a fully comprehensive flexible framework that does not rely on restrictive linearity and/or exogeneity assumptions. In order to examine the model, Turkey is selected as an inflation targeting developing economy, since its central bank (the Central Bank of Turkey) as argued by Dincer and Eichengreen (2014) has been one of the fastest improving central banks in terms of its transparency score. We use monthly data for the period of 2004-2018 that includes a number of historical episodes such as the global financial crisis as well as various internal political developments that may have had an impact on the fluctuations of the relevant macroeconomic variables as well as on the functional form of the inflation targeting Taylor rule specification. Empirical findings highlight the different reactions of the central bank in determining policy rate under different regimes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".