Türkiye’de sanayi üretim endeksinin zaman serileri yöntemi ile incelenmesi

Year 2018, Volume: 20 Issue: 1, 547 - 554, 13.05.2018

Hatice Öncel Çekim

https://doi.org/10.25092/baunfbed.423143

Cited By: 3

Abstract

Bu çalışmada, TÜİK tarafından 2005-2017 yılları arasında hesaplanan aylık sanayi üretim endeksi verilerine zaman serisi analizi uygulanmıştır. Çalışmanın amacı, sanayi üretim endeksini zaman serileri grafiği ile tanımlamak, endekse uygun zaman serisi modelini bulmak ve endeksin gelecek değerlerini tahmin etmektir. Bu amaçla, Box-Jenkins modellerinin uygulanabilmesi için serinin 1. dereceden fark ve 2. dereceden mevsimsel farkı alınarak seri durağan hale gelmiştir. Yapılan analizler sonucunda seriye en uygun model olarak SARIMA(1,1,1)(3,2,0)12 modeli belirlenmiştir. Bu model kullanılarak endeks serisinin 2018 yılı için aylık öngörü değerleri hesaplanmıştır.

Keywords

Zaman serileri, sanayi üretim endeksi, ARIMA yöntemi

Examination of industry production index in Turkey with time series method

Abstract

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)12 model 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.

Keywords

Time series, industrial production index, ARIMA method

References

• 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).
• Box, G. E. P. and Jenkıns, G. M., Time Series Analysis, Forecasting and Control, San Francisco: Holden-Day (1970).
• 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).
• Boero, G. and Lampis, F., The forecasting performance of SETAR models: an empirical application, Bulletin of Economic Research, 69, 3, 216-228, (2017).