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Time series modelling for steel production

Author

Listed:
  • Amir Ikram
  • Qin Su
  • Muhammad Yasir Rafiq
  • Ramiz-Ur-Rehman

    (Xi'an Jiaotong University, China
    University of Management and Technology, Pakistan
    University of Lahore, Pakistan)

Abstract

Steel and its production has now become an important ingredient for the economy of emerging countries. In this process, it is also very important to develop sophisticated forecast techniques for steel production in order to determine the real growth rate of an economy. Especially in South Asia where steel and its production, is one of the largest segment of the economy. Majority population of this region has associated directly or indirectly with steel industry. Inspite the importance of steel for this region, no conclusive research work has done related to this issue. This study investigates the forecasts of steel production in Pakistan for the first time, which is an important emerging economy of the South Asian region. Prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The auto regressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model.This paper forecasts steel production by using different time series ARIMA models. A data set of steel production of Pakistan from 1972 -2010 is used for the analysis. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (1,1,4) is suitable model for prediction of steel production in this case. It is concluded that model- 3 of this study is the best model to forecast the production of steel in Pakistan. After checking all the test it is come to know that the data is stationary at first difference and AR(1), MA(1), MA(2), MA(3) and MA(4) with first order is suitable for forecasting the production of steel. The forecasted values obtained from model- 3 are closer to the actual values as compared to the other model. The forecasted value of 2014 show that the production of steel in that particular year appeared to be least as compared to the production during the last five years. These results suggest that the policy makers and planning division of the country must give attention toward this matter and try to find out the reasons of low productivity of steel in the country. It is also an opportunity for the policy makers to develop policies which may help the steel production in future.

Suggested Citation

  • Amir Ikram & Qin Su & Muhammad Yasir Rafiq & Ramiz-Ur-Rehman, 2016. "Time series modelling for steel production," Journal of Developing Areas, Tennessee State University, College of Business, vol. 50(3), pages 191-207, July-Sept.
  • Handle: RePEc:jda:journl:vol.50:year:2016:issue3:pp:191-207
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    Cited by:

    1. Xiao, Shuang & Sethi, Suresh P. & Liu, Mengqi & Ma, Shihua, 2017. "Coordinating contracts for a financially constrained supply chain," Omega, Elsevier, vol. 72(C), pages 71-86.
    2. Buchholz, Thomas & Gunn, John S. & Saah, David S., 2017. "Greenhouse gas emissions of local wood pellet heat from northeastern US forests," Energy, Elsevier, vol. 141(C), pages 483-491.
    3. Lizana, Jesús & Chacartegui, Ricardo & Barrios-Padura, Angela & Ortiz, Carlos, 2018. "Advanced low-carbon energy measures based on thermal energy storage in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3705-3749.
    4. Gałaś, Slávka & Gałaś, Andrzej, 2016. "The qualification process of mining projects in environmental impact assessment: Criteria and thresholds," Resources Policy, Elsevier, vol. 49(C), pages 204-212.
    5. Lopez, Rigoberto A. & He, Xi & De Falcis, Eleonora, 2017. "What Drives China’s New Agricultural Subsidies?," World Development, Elsevier, vol. 93(C), pages 279-292.
    6. Brijs, Tom & De Jonghe, Cedric & Hobbs, Benjamin F. & Belmans, Ronnie, 2017. "Interactions between the design of short-term electricity markets in the CWE region and power system flexibility," Applied Energy, Elsevier, vol. 195(C), pages 36-51.

    More about this item

    Keywords

    Time series; production; Pakistan steel industry; forecasting;
    All these keywords.

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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