Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2019, Cilt: 7 Sayı: 1, 54 - 65, 28.02.2019
https://doi.org/10.31195/ejejfs.502397

Öz

Kaynakça

  • Akgül, I. (2003). Analysis of time series and ARIMA models. İstanbul: Der Publisher.
  • Akyüz, K.C., Yıldırım, İ. (2006). Paper and paperboard industry in European Union process. Kafkas University Forest Faculty Journal 7(2):159-171.
  • 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.
  • 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.
  • 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.
  • Chen, S.X., Lei, L., Tu, Y. (2014). Functional coefficient moving average model with applications to forecasting Chinese CPI. Statistica Sinica 1-33.
  • Co, C.H., Boosarawongse, R. (2007). Forecasting Thailand’s rice export: statistical techniques vs. artificial neural networks. Computers & Industrial Engineering 53:610-627.
  • 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.
  • 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.
  • Gavcar, E., Şen, S., Aytekin, A. (1999). Prediction forecasting of the papers used in Turkey. Turkish Journal of Agriculture and Forestry 23:203-211.
  • 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.
  • Griffiths, W.E., Hill, R.C., Judge, G.G. (1993). Learning and practicing econometrics. NewYork: John Wiley&Sons Inc.
  • Göktaş, Ö. (2005). Theoretical and applied time series analysis. İstanbul: Beşir Publisher.
  • Guajarati, D.N., Porter, D.C. (2012). Basic econometrics. (Translator: Ümit Şenesen and Gülay Günlük Şenesen). İstanbul: Literatür Publisher.Hamori, S. (2001). Seasonality and stock returns: some evidence from Japan. Japan and World Economy 13:463-481.
  • Hylleberg, S., Engle, R.F., Granger, C.W.J, Yoo, B.S. (1990). Seasonal integration and cointegration. Journal of Econometrics 44: 215-238.
  • Hyndman, R.J., Athanasopoulos, G. (2017). Forecasting: principles and practice. Otexts Publisher, Melbourne, Australia (OTexts.org/fpp2/).
  • Jackson, E.A., Sillah, A., Tamuke, E. (2018). Modelling monthly headline consumer price index (HCPI) through seasonal Box-Jenkins methodology. International Journal of Sciences 7:51-56.
  • Jeong, K., Koo, C., Hong, T. (2014). An Estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network). Energy 71:71-79.
  • Khashei, M., Bijari, M., Ardali, G.A.R. (2012). Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs). Computers and Industrial Engineering 63:37–45.
  • Kirchgassner, G., Wolters, J. (2007). Introduction to modern time series analysis. NewYork: Springer-Verlag Berlin Heidelberg Publisher.
  • Lewis CD (1982). Industrial and business forecasting methods. London: Butterworths Publishing.Ljung, G.M., Box, G.E.P. (1978). On a measure of lack of fit in time series model. Biometrica 65(2):297-303.
  • Meng, X. (2013). Testing for seasonal unit roots when residuals contain serial correlations under HEGY test framework. Report number: Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581; 3, Technical Report, 1-25.
  • Mishra, P., Dhekale, B., Sahu, P.K. (2018). Modelling and forecasting of Sunn Hemp in India. International Journal of Current Microbiology and Applied Sciences 6:1284-1293.
  • Pindyck, R.S., Rubinfeld, D.L. (1998). Econometric models and economic forecasts. USA: Irwin/McGraw-Hill Company.
  • Siregar, B., Nababan, E.B., Yap, A., Andayani, U., Fahmi, F. (2017). Forecasting of raw material needed for plastic products based in income data using ARIMA method. 5th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), Malang, Indonesia, 6-8 October, 135-139.
  • Sivri, U. (2004). Stochastic seasonality in Istanbul stock exchange. Marmara University Journal of Faculty of Economics and Administrative Sciences 19(1):195-208.
  • Tajdini, A., Tavakkoli, A., Latibari, J.A., Roohnia, M., Pourmousa, S. (2014). Statistical modeling to forecast the wood-based panels consumption in Iran. International Journal of Biosciences 4(12):1-11.
  • TSI. (2015a). Number of enterprises and employees in paper and paper products as of 2013. Retrieved from https://biruni.tuik.gov.tr/medas/?kn=63&locale=tr Last access date: 10 May 2015.
  • TSI. (2015b). Paper and board production and production value as of 2014. Retrieved from https://tuikappi.tuik.gov.tr/medas/?kn=63&locale=tr. Last access date: 10 May 2015.
  • TRADEMAP. (2015). Trade statistics for international business development, list 2014 of exporters and importers for the selected products 47 and 48. Retrieved from http://www.trademap.org/Country_SelProductCountry_TSaspx. Last access date: 10 May 2015.

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

Yıl 2019, Cilt: 7 Sayı: 1, 54 - 65, 28.02.2019
https://doi.org/10.31195/ejejfs.502397

Öz

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.


Kaynakça

  • Akgül, I. (2003). Analysis of time series and ARIMA models. İstanbul: Der Publisher.
  • Akyüz, K.C., Yıldırım, İ. (2006). Paper and paperboard industry in European Union process. Kafkas University Forest Faculty Journal 7(2):159-171.
  • 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.
  • 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.
  • 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.
  • Chen, S.X., Lei, L., Tu, Y. (2014). Functional coefficient moving average model with applications to forecasting Chinese CPI. Statistica Sinica 1-33.
  • Co, C.H., Boosarawongse, R. (2007). Forecasting Thailand’s rice export: statistical techniques vs. artificial neural networks. Computers & Industrial Engineering 53:610-627.
  • 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.
  • 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.
  • Gavcar, E., Şen, S., Aytekin, A. (1999). Prediction forecasting of the papers used in Turkey. Turkish Journal of Agriculture and Forestry 23:203-211.
  • 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.
  • Griffiths, W.E., Hill, R.C., Judge, G.G. (1993). Learning and practicing econometrics. NewYork: John Wiley&Sons Inc.
  • Göktaş, Ö. (2005). Theoretical and applied time series analysis. İstanbul: Beşir Publisher.
  • Guajarati, D.N., Porter, D.C. (2012). Basic econometrics. (Translator: Ümit Şenesen and Gülay Günlük Şenesen). İstanbul: Literatür Publisher.Hamori, S. (2001). Seasonality and stock returns: some evidence from Japan. Japan and World Economy 13:463-481.
  • Hylleberg, S., Engle, R.F., Granger, C.W.J, Yoo, B.S. (1990). Seasonal integration and cointegration. Journal of Econometrics 44: 215-238.
  • Hyndman, R.J., Athanasopoulos, G. (2017). Forecasting: principles and practice. Otexts Publisher, Melbourne, Australia (OTexts.org/fpp2/).
  • Jackson, E.A., Sillah, A., Tamuke, E. (2018). Modelling monthly headline consumer price index (HCPI) through seasonal Box-Jenkins methodology. International Journal of Sciences 7:51-56.
  • Jeong, K., Koo, C., Hong, T. (2014). An Estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network). Energy 71:71-79.
  • Khashei, M., Bijari, M., Ardali, G.A.R. (2012). Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs). Computers and Industrial Engineering 63:37–45.
  • Kirchgassner, G., Wolters, J. (2007). Introduction to modern time series analysis. NewYork: Springer-Verlag Berlin Heidelberg Publisher.
  • Lewis CD (1982). Industrial and business forecasting methods. London: Butterworths Publishing.Ljung, G.M., Box, G.E.P. (1978). On a measure of lack of fit in time series model. Biometrica 65(2):297-303.
  • Meng, X. (2013). Testing for seasonal unit roots when residuals contain serial correlations under HEGY test framework. Report number: Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581; 3, Technical Report, 1-25.
  • Mishra, P., Dhekale, B., Sahu, P.K. (2018). Modelling and forecasting of Sunn Hemp in India. International Journal of Current Microbiology and Applied Sciences 6:1284-1293.
  • Pindyck, R.S., Rubinfeld, D.L. (1998). Econometric models and economic forecasts. USA: Irwin/McGraw-Hill Company.
  • Siregar, B., Nababan, E.B., Yap, A., Andayani, U., Fahmi, F. (2017). Forecasting of raw material needed for plastic products based in income data using ARIMA method. 5th International Conference on Electrical, Electronics and Information Engineering (ICEEIE), Malang, Indonesia, 6-8 October, 135-139.
  • Sivri, U. (2004). Stochastic seasonality in Istanbul stock exchange. Marmara University Journal of Faculty of Economics and Administrative Sciences 19(1):195-208.
  • Tajdini, A., Tavakkoli, A., Latibari, J.A., Roohnia, M., Pourmousa, S. (2014). Statistical modeling to forecast the wood-based panels consumption in Iran. International Journal of Biosciences 4(12):1-11.
  • TSI. (2015a). Number of enterprises and employees in paper and paper products as of 2013. Retrieved from https://biruni.tuik.gov.tr/medas/?kn=63&locale=tr Last access date: 10 May 2015.
  • TSI. (2015b). Paper and board production and production value as of 2014. Retrieved from https://tuikappi.tuik.gov.tr/medas/?kn=63&locale=tr. Last access date: 10 May 2015.
  • TRADEMAP. (2015). Trade statistics for international business development, list 2014 of exporters and importers for the selected products 47 and 48. Retrieved from http://www.trademap.org/Country_SelProductCountry_TSaspx. Last access date: 10 May 2015.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Articles
Yazarlar

Nadir Ersen 0000-0003-3643-1390

İlker Akyüz 0000-0003-4241-1118

Bahadır Çağrı Bayram 0000-0002-8563-0233

Yayımlanma Tarihi 28 Şubat 2019
Gönderilme Tarihi 25 Aralık 2018
Yayımlandığı Sayı Yıl 2019 Cilt: 7 Sayı: 1

Kaynak Göster

APA 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, 7(1), 54-65. https://doi.org/10.31195/ejejfs.502397

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