SavedAdd your journal

Journals / Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2017 / Cilt: 32 - Sayı: 2

Türkiye İçin Enflasyon Oranının Uygun Mevsimsel ARIMA Modeli İle Belirlenmesi

Detecting the Best Seasonal ARIMA Forecasting Model for Monthly Inflation Rates in Turkey1

Mehmet ÖZMEN
Sera ŞANLI
Pages
143–182
Publication Date
2022-10-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results have shownthat the former model is better as the bestfitted SARIMA model.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, it has been aimed to find the best ‘Seasonal Autoregressive IntegratedMoving Average (SARIMA) modelfor monthly inflation rates for Turkish economy over theperiod 995:01 -201 :03 Before the model identification based on Box Jenkins methodology,HEGY monthly seasonal unit root test has been applied.
📊
Key Findings & Data:Finally, ARIM4(1,1,1)(1,0,2)[12] with driftmodel chosen by using stepwise selection method and ARIMA(1,1,1)(2,0,0)[12] with driftmodel chosen by using non-stepwise selection have been compared.
💡
Conclusion & Impact:The results have shownthat the former model is better as the bestfitted SARIMA model.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this study, it has been aimed to find the best ‘Seasonal Autoregressive IntegratedMoving Average (SARIMA) modelfor monthly inflation rates for Turkish economy over theperiod 995:01 -201 :03 Before the model identification based on Box Jenkins methodology,HEGY monthly seasonal unit root test has been applied. The orders of seasonal difi‘erencinghave been detected through OCSB and CH tests. Finally, ARIM4(1,1,1)(1,0,2)[12] with driftmodel chosen by using stepwise selection method and ARIMA(1,1,1)(2,0,0)[12] with driftmodel chosen by using non-stepwise selection have been compared. The results have shownthat the former model is better as the bestfitted SARIMA model.

Özet

Bu çalışmada Türkiye'nin 1995.'01--2015:03 dönemine ilişkin aylık enflasyon oranlarıiçin en iyi Mevsimsel Otoregresif Bütünleşik Hareketli Ortalama (SARIM4)' modelinibulmak amaçlanmıştır. Box Jenkins metodolojisine dayalı model tanımlamasından önceHEGYmevsimsel birim kök testi uygulanmıştır. Mevsimselfark alma dereceleri OCSB ve CHtestleri kullanılarak saptanmıştır. Son olarak, sırasıyla adımsal (stepwise) ve adımsalolmayan (non-stepwise) seçim yöntemleri kullanılarak seçilen sürüklenmeliARIM4(1,1,1)(1,0,2)[12] ve ARIIMA( ,1 ,I )(2,0, 0)[ modelleri karşılaştırılmıştır. Sonuçlaradımsal yöntem kullanılarak seçilen sürüklenmeli ARIM4(1,1,1)(1,0,2)[12] modelinin en iyiuyan SARIIWA modelini belirlemede adımsal olmayan modele göre daha iyi olduğunugöstermiştir.

🔖

Cite This Article

ÖZMEN, M., ŞANLI, S. (2022). Türkiye İçin Enflasyon Oranının Uygun Mevsimsel ARIMA Modeli İle Belirlenmesi. Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Cilt: 32 - Sayı: 2, 143–182. https://ajindex.com/articles/490516

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (10)

  1. ABRAHAM, B., BOX, G. E. P. (1978), “Deterministic and Forecast-Adaptive Time-Dependent Models”, Applied Statistics, 27(2), 120-130.
  2. AIDOO, E. (2010), Modelling and forecasting inflation rates in Ghana: An application of SARIMA models. Master’s Thesis, Högskolan Dalarna School of Technology & Business Studies, Sweden. AKUFFO, B., AMPAW, E. M. (2013), “An Autoregressive Integrated Moving Average (ARIMA) Model for Ghana’s Inflation (1985-2011)”, Mathematical Theory and Modelling, 3(3), 10-26.
  3. BEAULIEU, J. J., MIRON, J. A. (1992), Seasonal Unit Roots in Aggregate U.S. Data (NBER Technical Paper No. 126). Cambridge: National Bureau of Economic Research. BEAULIEU, J. J., MIRON, J. A. (1993), “Seasonal Unit Roots in Aggregate U.S. Data”, Journal of Econometrics, 55(1-2), 305-328.
  4. BOX, G. E. P., JENKINS, G. M. (1970), Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco. BOX, G. E. P., JENKINS, G. M. (1976), Time Series Analysis: Forecasting and Control (2 nd ed.), Holden-Day, San Francisco. BROCKWELL, P. J., DAVIS, R. A. (2002), Introduction to Time Series and Forecasting (2nd ed.), Springer-Verlag, New York. BROCKWELL, P. J., DAVIS, R. A. (2006), Time Series: Theory and Methods (2nd ed.), Springer, New York. CANOVA, F., HANSEN, B. E. (1995), “Are Seasonal Patterns Constant Over Time? A Test for Seasonal Stability”, Journal of Business and Economic Statistics, 13(3), 237-252.
  5. CHANG, Y. W., LIAO, M. Y. (2010), “A Seasonal ARIMA Model of Tourism Forecasting: The Case of Taiwan”, Asia Pacific Journal of Tourism Research, 15(2), 215-221.
  6. CHATFIELD, C. (1996), The Analysis of Time Series: An Introduction (5th ed.), Chapman & Hall/CRC, London, UK. CHEN, R., SCHULZ, R., STEPHAN, S. (2003), “Multiplicative SARIMA Models”, Computer-Aided Introduction to Econometrics, (Ed. J.R. Poo), Berlin: Springer-Verlag, 225-254.
  7. COSAR, E. E. (2006), “Seasonal Behaviour of the Consumer Price Index of Turkey”, Applied Economics Letters, 13(7), 449-455.
  8. DIAZ-EMPARANZA, I., LOPEZ-de-LACALLE, J. (2006), “Testing for Unit Roots in Seasonal Time Series with R: The Uroot Package”, http://www.jalobe.com:8080/doc/uroot.pdf, (10.05.2015).
  9. DICKEY, D., HASZA, D., FULLER, W. (1984), “Testing for Unit Roots in Seasonal Time Series”, Journal of the American Statistical Association, 79(386), 355-367.
  10. FRANSES, P. H. (1991), Model Selection and Seasonality in Time Series. Doctoral dissertation, Erasmus University Rotterdam, Netherlands. Retrieved from http://hdl.handle.net/1765/2047.