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

Yıl 2017, Cilt: 32 Sayı: 2, 143 - 182, 04.12.2017
https://doi.org/10.24988/deuiibf.2017322602

Öz

In this study, it has been aimed to find the best ‘Seasonal Autoregressive Integrated Moving Average (SARIMA)’ model for monthly inflation rates for Turkish economy over the period 1995:01-2015:03. Before the model identification based on Box Jenkins methodology, HEGY monthly seasonal unit root test has been applied. The orders of seasonal differencing have been detected through OCSB and CH tests. Finally, ARIMA(1,1,1)(1,0,2)[12] with drift model chosen by using stepwise selection method and ARIMA(1,1,1)(2,0,0)[12] with drift model chosen by using non-stepwise selection have been compared. The results have shown that the former model is better as the best fitted SARIMA model.

Kaynakça

  • ABRAHAM, B., BOX, G. E. P. (1978), “Deterministic and Forecast-Adaptive Time-Dependent Models”, Applied Statistics, 27(2), 120-130.
  • 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.
  • 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.
  • 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 (2nd 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.
  • 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.
  • 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.
  • COSAR, E. E. (2006), “Seasonal Behaviour of the Consumer Price Index of Turkey”, Applied Economics Letters, 13(7), 449-455.
  • 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).
  • 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.
  • 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.
  • FRANSES, P. H. (1998), “Modeling Seasonality in Economic Time Series”, Handbook of Applied Economic Statistics, (Eds. A. Ullah and D.E.A. Giles), New York: Marcel Dekker, 553-577.
  • FRANSES, P. H., HOBIJN, B. (1997), “Critical Values for Unit Root Tests in Seasonal Time Series”, Journal of Applied Statistics, 24(1), 25-48.
  • FRANSES, P. H., KOEHLER, A. B. (1998), “A Model Selection Strategy for Time Series with Increasing Seasonal Variation”, International Journal of Forecasting, 14(3), 405-414.
  • HAMAKER, E. L., DOLAN, C. V. (2009), “Idiographic Data Analysis: Quantitative Methods - from Simple to Advanced”, Dynamic Process Methodology in the Social and Developmental Sciences, (Eds. J. Valsiner, P. C. M. Molenaar, M. C. D. P. Lyra and N. Chaudhary), New York: Springer-Verlag, 191-216.
  • HASZA, D. P., FULLER, W. A. (1982), “Testing for Nonstationary Parameter Specifications in Seasonal Time Series Models”, The Annals of Statistics, 10(4), 1209-1216.
  • HILLMER, S. C., BELL, W. R., TIAO, G. C. (1983), “Modeling Considerations in the Seasonal Adjustment of Economic Time Series”, Applied Time Series Analysis of Economic Data, (Ed. A. Zellner), Washington, DC: U.S. Bureau of the Census, 74-100.
  • HYLLEBERG, S., ENGLE, R., GRANGER, C., YOO, S. (1990), “Seasonal Integration and Cointegration”, Journal of Econometrics, 44(1), 215-238.
  • HYNDMAN, R., J. (2014), “Plotting the Characteristic Roots for ARIMA Models”, http://robjhyndman.com/hyndsight/arma-roots/, (01.08.2015).
  • HYNDMAN, R., J. (2015, May), “Package ‘Forecast’”, http://cran.r-project.org/web/packages/forecast/forecast.pdf, (02.06.2015).
  • KWIATKOWSKI, D., PHILLIPS, P. C. B., SCHMIDT, P., SHIN, Y. (1992), “Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We That Economic Time Series Have a Unit Root?”, Journal of Econometrics, 54(1-3), 159-178.
  • LIM, C., MCALEER, M. (2000), “A Seasonal Analysis of Asian Tourist Arrivals to Australia”, Applied Economics, 32(4), 499-509.
  • MADDALA, G. S., KIM, I. M. (1998), Unit Roots, Cointegration and Structural Change, Cambridge University Press, Cambridge.
  • ORD, K., FILDES, R. (2013), Principles of Business Forecasting, South-Western, Cengage Learning.
  • OSBORN, D.R., CHUI, A. P. L., SMITH, J. P., BIRCHENHALL, C. R. (1988), “Seasonality and the Order of Integration for Consumption”, Oxford Bulletin of Economics and Statistics, 50(4), 361-377.
  • PANKRATZ, A. (1983), Forecasting with Univariate Box-Jenkins Model: Concepts and Cases, John Wiley & Sons, New York.
  • PLATON, V. (2010), “Application of Seasonal Unit Roots Tests and Regime Switching Models to the Prices of Agricultural Products in Moscow 1884-1913”, http://www.hse.ru/data/2010/10/22/1222675037/Seasonal%20unit%20roots%20and%20regime%20switch.pdf, (04.01.2015).
  • SANLI, S. (2015), The Econometric Analysis of Seasonal Time Series: Applications on Some Macroeconomic Variables, Master’s Thesis, Cukurova University, Adana.
  • SAZ, G. (2011), “The Efficacy of SARIMA Models for Forecasting Inflation Rates in Developing Countries: The Case for Turkey”, International Research Journal of Finance and Economics, 62, 111-142.
  • SHUMWAY, R. H., STOFFER, D. S. (2011), Time Series Analysis and Its Applications - with R Examples (3rd ed.), Springer, New York.
  • SØRENSEN, N. K. (2001), “Modelling the Seasonality of Hotel Nights in Denmark by County and Nationality”, Seasonality in Tourism, (Eds. T. Baum and S. Lundtrop), Oxford: Elsevier, 75-88.
  • TAM, W. K., REINSEL, G. C. (1997), “Tests for Seasonal Moving Average Unit Root in ARIMA Models”, Journal of the American Statistical Association, 92(438), 725-738.
  • ZHANG, Q. (2008), Seasonal Unit Root Tests: A Comparison. Doctoral Dissertation. North Carolina State University, Raleigh.

Türkiye İçin Enflasyon Oranının Uygun Mevsimsel ARIMA Modeli İle Belirlenmesi

Yıl 2017, Cilt: 32 Sayı: 2, 143 - 182, 04.12.2017
https://doi.org/10.24988/deuiibf.2017322602

Öz

Bu çalışmada Türkiye’nin 1995:01-2015:03 dönemine ilişkin aylık enflasyon oranları için en iyi ‘Mevsimsel Otoregresif Bütünleşik Hareketli Ortalama (SARIMA)’ modelini bulmak amaçlanmıştır. Box Jenkins metodolojisine dayalı model tanımlamasından önce HEGY mevsimsel birim kök testi uygulanmıştır. Mevsimsel fark alma dereceleri OCSB ve CH testleri kullanılarak saptanmıştır. Son olarak, sırasıyla adımsal (stepwise) ve adımsal olmayan (non-stepwise) seçim yöntemleri kullanılarak seçilen sürüklenmeli ARIMA(1,1,1)(1,0,2)[12] ve ARIMA(1,1,1)(2,0,0)[12] modelleri karşılaştırılmıştır. Sonuçlar adımsal yöntem kullanılarak seçilen sürüklenmeli ARIMA(1,1,1)(1,0,2)[12] modelinin en iyi uyan SARIMA modelini belirlemede adımsal olmayan modele göre daha iyi olduğunu göstermiştir.

Kaynakça

  • ABRAHAM, B., BOX, G. E. P. (1978), “Deterministic and Forecast-Adaptive Time-Dependent Models”, Applied Statistics, 27(2), 120-130.
  • 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.
  • 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.
  • 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 (2nd 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.
  • 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.
  • 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.
  • COSAR, E. E. (2006), “Seasonal Behaviour of the Consumer Price Index of Turkey”, Applied Economics Letters, 13(7), 449-455.
  • 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).
  • 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.
  • 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.
  • FRANSES, P. H. (1998), “Modeling Seasonality in Economic Time Series”, Handbook of Applied Economic Statistics, (Eds. A. Ullah and D.E.A. Giles), New York: Marcel Dekker, 553-577.
  • FRANSES, P. H., HOBIJN, B. (1997), “Critical Values for Unit Root Tests in Seasonal Time Series”, Journal of Applied Statistics, 24(1), 25-48.
  • FRANSES, P. H., KOEHLER, A. B. (1998), “A Model Selection Strategy for Time Series with Increasing Seasonal Variation”, International Journal of Forecasting, 14(3), 405-414.
  • HAMAKER, E. L., DOLAN, C. V. (2009), “Idiographic Data Analysis: Quantitative Methods - from Simple to Advanced”, Dynamic Process Methodology in the Social and Developmental Sciences, (Eds. J. Valsiner, P. C. M. Molenaar, M. C. D. P. Lyra and N. Chaudhary), New York: Springer-Verlag, 191-216.
  • HASZA, D. P., FULLER, W. A. (1982), “Testing for Nonstationary Parameter Specifications in Seasonal Time Series Models”, The Annals of Statistics, 10(4), 1209-1216.
  • HILLMER, S. C., BELL, W. R., TIAO, G. C. (1983), “Modeling Considerations in the Seasonal Adjustment of Economic Time Series”, Applied Time Series Analysis of Economic Data, (Ed. A. Zellner), Washington, DC: U.S. Bureau of the Census, 74-100.
  • HYLLEBERG, S., ENGLE, R., GRANGER, C., YOO, S. (1990), “Seasonal Integration and Cointegration”, Journal of Econometrics, 44(1), 215-238.
  • HYNDMAN, R., J. (2014), “Plotting the Characteristic Roots for ARIMA Models”, http://robjhyndman.com/hyndsight/arma-roots/, (01.08.2015).
  • HYNDMAN, R., J. (2015, May), “Package ‘Forecast’”, http://cran.r-project.org/web/packages/forecast/forecast.pdf, (02.06.2015).
  • KWIATKOWSKI, D., PHILLIPS, P. C. B., SCHMIDT, P., SHIN, Y. (1992), “Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We That Economic Time Series Have a Unit Root?”, Journal of Econometrics, 54(1-3), 159-178.
  • LIM, C., MCALEER, M. (2000), “A Seasonal Analysis of Asian Tourist Arrivals to Australia”, Applied Economics, 32(4), 499-509.
  • MADDALA, G. S., KIM, I. M. (1998), Unit Roots, Cointegration and Structural Change, Cambridge University Press, Cambridge.
  • ORD, K., FILDES, R. (2013), Principles of Business Forecasting, South-Western, Cengage Learning.
  • OSBORN, D.R., CHUI, A. P. L., SMITH, J. P., BIRCHENHALL, C. R. (1988), “Seasonality and the Order of Integration for Consumption”, Oxford Bulletin of Economics and Statistics, 50(4), 361-377.
  • PANKRATZ, A. (1983), Forecasting with Univariate Box-Jenkins Model: Concepts and Cases, John Wiley & Sons, New York.
  • PLATON, V. (2010), “Application of Seasonal Unit Roots Tests and Regime Switching Models to the Prices of Agricultural Products in Moscow 1884-1913”, http://www.hse.ru/data/2010/10/22/1222675037/Seasonal%20unit%20roots%20and%20regime%20switch.pdf, (04.01.2015).
  • SANLI, S. (2015), The Econometric Analysis of Seasonal Time Series: Applications on Some Macroeconomic Variables, Master’s Thesis, Cukurova University, Adana.
  • SAZ, G. (2011), “The Efficacy of SARIMA Models for Forecasting Inflation Rates in Developing Countries: The Case for Turkey”, International Research Journal of Finance and Economics, 62, 111-142.
  • SHUMWAY, R. H., STOFFER, D. S. (2011), Time Series Analysis and Its Applications - with R Examples (3rd ed.), Springer, New York.
  • SØRENSEN, N. K. (2001), “Modelling the Seasonality of Hotel Nights in Denmark by County and Nationality”, Seasonality in Tourism, (Eds. T. Baum and S. Lundtrop), Oxford: Elsevier, 75-88.
  • TAM, W. K., REINSEL, G. C. (1997), “Tests for Seasonal Moving Average Unit Root in ARIMA Models”, Journal of the American Statistical Association, 92(438), 725-738.
  • ZHANG, Q. (2008), Seasonal Unit Root Tests: A Comparison. Doctoral Dissertation. North Carolina State University, Raleigh.
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Makaleler
Yazarlar

Mehmet Özmen

Sera Şanlı

Yayımlanma Tarihi 4 Aralık 2017
Kabul Tarihi 18 Haziran 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 32 Sayı: 2

Kaynak Göster

APA Özmen, M., & Şanlı, S. (2017). Detecting the Best Seasonal ARIMA Forecasting Model for Monthly Inflation Rates in Turkey. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 32(2), 143-182. https://doi.org/10.24988/deuiibf.2017322602
AMA Özmen M, Şanlı S. Detecting the Best Seasonal ARIMA Forecasting Model for Monthly Inflation Rates in Turkey. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi. Aralık 2017;32(2):143-182. doi:10.24988/deuiibf.2017322602
Chicago Özmen, Mehmet, ve Sera Şanlı. “Detecting the Best Seasonal ARIMA Forecasting Model for Monthly Inflation Rates in Turkey”. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi 32, sy. 2 (Aralık 2017): 143-82. https://doi.org/10.24988/deuiibf.2017322602.
EndNote Özmen M, Şanlı S (01 Aralık 2017) Detecting the Best Seasonal ARIMA Forecasting Model for Monthly Inflation Rates in Turkey. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi 32 2 143–182.
IEEE M. Özmen ve S. Şanlı, “Detecting the Best Seasonal ARIMA Forecasting Model for Monthly Inflation Rates in Turkey”, Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, c. 32, sy. 2, ss. 143–182, 2017, doi: 10.24988/deuiibf.2017322602.
ISNAD Özmen, Mehmet - Şanlı, Sera. “Detecting the Best Seasonal ARIMA Forecasting Model for Monthly Inflation Rates in Turkey”. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi 32/2 (Aralık 2017), 143-182. https://doi.org/10.24988/deuiibf.2017322602.
JAMA Özmen M, Şanlı S. Detecting the Best Seasonal ARIMA Forecasting Model for Monthly Inflation Rates in Turkey. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi. 2017;32:143–182.
MLA Özmen, Mehmet ve Sera Şanlı. “Detecting the Best Seasonal ARIMA Forecasting Model for Monthly Inflation Rates in Turkey”. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, c. 32, sy. 2, 2017, ss. 143-82, doi:10.24988/deuiibf.2017322602.
Vancouver Özmen M, Şanlı S. Detecting the Best Seasonal ARIMA Forecasting Model for Monthly Inflation Rates in Turkey. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi. 2017;32(2):143-82.