Examination of industry production index in Turkey with time series method

Hatice Öncel Çekim

Öz


In this paper, the time series analysis is conducted to the monthly industrial production index data calculated between 2005 and 2017 by TURKSTAT. The aim of the study is to define the industrial production index with the time series chart, to find the suitable time series model for the index and to forecast the future values of the index. For this purpose, we make the series stationary by taking both the first difference and the second seasonal difference of the series to perform the Box-Jenkins models. As a result of the analysis, SARIMA(1,1,1)(3,2,0)12model is determined as the most suitable model for the series. Using this model, the forecast values for the months of 2018 of the index series are calculated.


Tam Metin:

PDF

Referanslar


. Öcal, F. M., Türkiye’de sanayi üretim endeksi ve imalat sanayi eğilim göstergeleri arasındaki ilişkinin ekonometrik analizi, CBÜ Sosyal Bilimler Dergisi, 11, 2, 242-258, (2013).

. Moody, J., Levin, U. and Rehfuss, S., Predicting the U.S. index of industrial production, Proceedings , PASE ‘93, Parallel applications in statistics and economics, 791–794, Netherlands, (1993).

. Marchetti, D. J. and Parigi, G., Energy consumption, survey data and the prediction of industrial production in Italy: A comparison and combination of different models, Journal of Forecast, 19, 419-440, (2000).

. Hassani, H., Heravi, S. and Zhigljavsky, A., Forecasting Europan industrial production with singular spectrum analysis, International Journal of Forecasting, 25, 103-118, (2009).

. Mazur, B., Density forecasts of polish industrial production: a probabilistic perspective on business cycle fluctuations, Institute of economic research working papers, 75, Poland, (2017).

. Ulbricht, D., Kholodilin, K. A. and Thomas, T., Do Media Data Help to Predict German Industrial Production?, Journal of Forecasting, 36, 5, 483-496, (2017).

. Frances P. H., Seasonality, non-seasonality and the forecasting of monthly time series, International Journal of Forecasting, 7, 199-208, (1991).

. Bodo, G., Golinelli, R. and Parigi, G., Forecasting industrial production in the euro area, Empirical economics, 25, 4, 541-561, (2000).

. Bulligan, G., Golinelli, R. and Parigi, G., Forecasting monthly industrial production in real-time: from single equations to factor-based models. Empirical Economics, 39, 2, 303-336, (2010).

. Zhigljavsky, A., Hassani, H. and Heravi, S., Forecasting European Industrial Production with Multivariate Singular Spectrum Analysis, Business, 1–39, (2009).

. Çekim, H. Ö., Kadılar, C. and Özel, G., Characterizing forest fire activity in Turkey by compound Poisson and time series models, In AIP Conference Proceedings, 1558, 1442-1445, (2013).

. Guarnaccia, C., Quartieri, J. and Tepedino, C. Deterministic decomposition and seasonal ARIMA time series models applied to airport noise forecasting, In AIP Conference Proceedings, 020079, 1-7, (2017).

. Chatfield, C., Time series forecasting, 92-103, Chapman & Hall/CRC, Florida, (2000).

. Kadılar, C., SPSS uygulamalı zaman serileri analizine giriş, 185-235, Bizim Büro Basımevi, Ankara, (2009).

. Cryer, J. D. and Chan, K. S., Time series analysis with applications in R, 92-108, Springer, USA, (2008).

. Boero, G. and Lampis, F., The forecasting performance of SETAR models: an empirical application, Bulletin of Economic Research, 69, 3, 216-228, (2017).


Refback'ler

  • Şu halde refbacks yoktur.


Telif Hakkı (c) 2018 Hatice Öncel Çekim

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.