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Journals / Eurasian Journal of Forest Science / 2019 / Cilt: 7 - Sayı: 1

The forecasting of the exports and imports of paper and paper products of Turkey using Box-Jenkins method

Nadir ERSEN
İlker AKYÜZ
Bahadır Çağrı BAYRAM
Pages
54–65
Publication Date
2019-02-28
DOI
—
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AI Summary & Key Takeaways

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

It was predicted that the rate of exports meeting imports in paper and paper products of Turkey will be approximately 0.86 in 2020.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, it is aimed to determine the most suitable time series models with Box-Jenkins method, which is the most widely used in prediction studies.
📊
Key Findings & Data:Export and import values have been predicted by 2020 with the most suitable models.
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Conclusion & Impact:It was predicted that the rate of exports meeting imports in paper and paper products of Turkey will be approximately 0.86 in 2020.
🔬Design:Modeling & Simulation
📍Scope:2020 Verileri
⚙️Variables:Paper and paper products · Box-Jenkins · Exports and Imports

Abstract

In this study, it is aimed to determine the most suitable time series models with Box-Jenkins method, which is the most widely used in prediction studies. Export and import values have been predicted by 2020 with the most suitable models. The data used in this study were obtained from the Turkey Statistical Institute. Data are monthly data covering from January 2003 to December 2014. Sum of Squared Errors (SSE) and Mean Squared Error (MSE) criteria were taken into consideration when selecting the best Box-Jenkins models. Also, in order to test the success of forecasting of the models, Root mean Error Square (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used. As a result of the analyzes, it was determined that the most suitable models for export and import data were ARIMA (2,1,0) (0,0,1)12 and ARIMA(3,1,2)(1,0,1)12. It was predicted that the rate of exports meeting imports in paper and paper products of Turkey will be approximately 0.86 in 2020.

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Cite This Article

ERSEN, N., AKYÜZ, İ., BAYRAM, B. Ç. (2019). The forecasting of the exports and imports of paper and paper products of Turkey using Box-Jenkins method. Eurasian Journal of Forest Science, Cilt: 7 - Sayı: 1, 54–65. https://ajindex.com/articles/665112

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References (13)

  1. Akgül, I. (2003). Analysis of time series and ARIMA models. İstanbul:
  2. Der Publisher.
  3. Akyüz, K.C., Yıldırım, İ. (2006). Paper and paperboard industry in European Union process. Kafkas University Forest Faculty Journal 7(2):159-171.
  4. Akyüz, K.C., Yıldırım, İ., Akyüz, İ. Tugay, T. (2017). Assessing the financial failure of paper and paper products industry enterprises at stock exchange Istanbul using the ratio and discriminant analyses. Düzce University Journal of Forestry 13(1):60-74.Arltova, M., Fedorova, D. (2016). Selection of unit root test on the basis of length of the time series and value of AR(1) parameter. Statistika 96(3):47-64.
  5. Atalay, G. (2012). The analysis of target market tendency in foreign trade of turkish forest products. [Master Thesis] İstanbul University Institute of Natural and Applied Sciences, Istanbul. Bayraktar, F. (2014). Manufacture of pulp, paper and paper products. Republic of Turkey Ministry of Development, Ankara, pp. 331-374.
  6. Beaulieu, J.J., Miron, J.A. (1992). Seasonal unit roots in aggregate U.S. data. National Bureau of Economic Research, Technical Paper No: 126. Box, G.E.P., Jenkins, G.M. (1970). Distribution of the residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association 65:1509-1526.
  7. Chen, S.X., Lei, L., Tu, Y. (2014). Functional coefficient moving average model with applications to forecasting Chinese CPI. Statistica Sinica 1-33.
  8. Co, C.H., Boosarawongse, R. (2007). Forecasting Thailand’s rice export: statistical techniques vs. artificial neural networks. Computers & Industrial Engineering 53:610-627.
  9. Emang, D., Shitan, M., Ghani, A.N.A, Noor, M.K. (2010). Forecasting with univariate time series models: a case of export demand for Peninsular Malaysia’s moulding and chipboard. Journal of Sustainable Development 383:157-161.
  10. Franses, P.H. (1990). Testing for seasonal unit roots in monthly data. Technical Report 9032/A of the Econometric Institute, Erasmus University, Rotterdam. Franses, P.H., Hobjin, B. (1997). Critical values for unit root tests in seasonal time series. Journal of Applied Statistics 24:25-46.
  11. Gavcar, E., Şen, S., Aytekin, A. (1999). Prediction forecasting of the papers used in Turkey. Turkish Journal of Agriculture and Forestry 23:203-211.
  12. Gedik, T., Akyüz, K.C., Ustaömer, D. (2005). The portions of forest product industry in the foreign trade of Turkey. Kafkas University Forest Faculty Journal 6(1-2): 171-178.
  13. Griffiths, W.E