An empirical analysis of inflation targeting: Turkish case
Bibliographic record
Abstract
Son yıllarda birçok para politikalarının başarısız olması politikacıları fiyat istikrarını sağlayacak yeni bir para politikası bulmaya zorlamıştır. 1990 yılında Yeni Zelanda 'enflasyon hedeflemesi adında yeni bir para politikası uygulamaya başladı. Onu İngiltere, Kanada, Şili ve diğerleri takip etti. Enflasyon hedeflemesi para politikasında yeni bir kavram olduğu için bu yeni para politikasını incelemek para politikası ile ilgilenenleri aydınlatacaktır. Enflasyon hedeflemesi her ülkede aynı uygulanan bir para politikası olmadığı için bu çalışma bize paha biçilmez bir öngörü sağlayacak çeşitli ülke örneklerini incelemektedir. Ayrıca, enflasyon tahmini enflasyon hedeflemesinde önemli bir yer tutmaktadır. Bu tez Vector Error Correction Modeli (VECM) kullanarak Türkiye'deki enflasyonu tahmin etme gücüne sahip değişkenleri belirlemiştir. Sanayi Üretim Endeksi ve Cari Dolar Kuru kullanılarak enflasyon tahmin modeli oluşturulmuştur. Sonuçlar Türkiye'deki enflasyonun (Tüketici Fiyat Endeksi) 16 aylık bir dönem içerisinde %95 güven aralığında tahmin edilebileceğine göstermektedir. The failure of conventional types of monetary policy in recent experience led economists to search a new type of monetary policy which will ensure price stability. In 1990, New Zealand started to implement a new monetary policy regime called 'inflation targeting'. United Kingdom, Canada, Chile and much more followed New Zealand soon after. Since inflation targeting is rather a new concept in monetary policy, investigating this type of policy may enlighten people interested in monetary policy. It is important that the implementation of inflation targeting be not a one fits all regime. Therefore, this study examines the different country experiences which will provide invaluable insight for us. Moreover, inflation forecasting plays a key role in inflation targeting regime. This thesis identifies the variables that have predictive power to forecast inflation for Turkey using Vector Error Correction Model (VECM). By using Industrial Production Index and Nominal (dollar) Exchange Rate, this thesis forms a forecast model. The results indicate that inflation rates (Consumer Price Index) in Turkey can be forecasted for 16 months time horizon with 95% confidence.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".