IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i19p6021-d640419.html
   My bibliography  Save this article

Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models

Author

Listed:
  • Palanisamy Manigandan

    (Department of Statistics, Periyar University, Salem P.O. Box 636011, Tamil Nadu, India)

  • MD Shabbir Alam

    (Department of Economics & Finance, College of Business Administration, University of Bahrain, Zallaq P.O. Box 2038, Bahrain)

  • Majed Alharthi

    (Finance Department, College of Business, King Abdulaziz University, Rabigh 21911, Saudi Arabia)

  • Uzma Khan

    (Department of Finance, College of Business Administration, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

  • Kuppusamy Alagirisamy

    (Department of Statistics, Periyar University, Salem P.O. Box 636011, Tamil Nadu, India)

  • Duraisamy Pachiyappan

    (Department of Statistics, Periyar University, Salem P.O. Box 636011, Tamil Nadu, India)

  • Abdul Rehman

    (College of Economics and Management, Henan Agricultural University, Zhengzhou 450002, China)

Abstract

Research on forecasting the seasonality and growth trend of natural gas (NG) production and consumption will help organize an analysis base for NG inspection and development, social issues, and allow industrials elements to operate effectively and reduce economic issues. In this situation, we handle a comparison structure on the application of different models in monthly NG production and consumption forecasting using the cross-correlation function and then analyze the association between exogenous variables. Moreover, the SARIMA-X model is tested for US monthly NG production and consumption prediction via the proposed method for the first time in the literature review in this study. The performance of that model has been compared with SARIMA ( p , d , q ) * ( P , D , Q ) s . The results from RMSE and MAPE indicate that the superiority of the best model. By applying this method, the US monthly NG production and consumption is forecast until 2025. The success of the proposed method allows the use of seasonality patterns. If this seasonal approach continues, the United States’ NG production (16%) and consumption (24%) are expected to increase by 2025. The results of this study provide effective information for decision-makers on NG production and consumption to be credible and to determine energy planning and future sustainable energy policies.

Suggested Citation

  • Palanisamy Manigandan & MD Shabbir Alam & Majed Alharthi & Uzma Khan & Kuppusamy Alagirisamy & Duraisamy Pachiyappan & Abdul Rehman, 2021. "Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models," Energies, MDPI, vol. 14(19), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6021-:d:640419
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/19/6021/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/19/6021/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sen, Doruk & Günay, M. Erdem & Tunç, K.M. Murat, 2019. "Forecasting annual natural gas consumption using socio-economic indicators for making future policies," Energy, Elsevier, vol. 173(C), pages 1106-1118.
    2. G. E. P. Box & G. M. Jenkins & J. F. MacGregor, 1974. "Some Recent Advances in Forecasting and Control," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 23(2), pages 158-179, June.
    3. Bierens, Herman J., 1987. "Armax model specification testing, with an application to unemployment in the Netherlands," Journal of Econometrics, Elsevier, vol. 35(1), pages 161-190, May.
    4. Ravnik, J. & Hriberšek, M., 2019. "A method for natural gas forecasting and preliminary allocation based on unique standard natural gas consumption profiles," Energy, Elsevier, vol. 180(C), pages 149-162.
    5. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    6. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    7. Tarsitano, Agostino & Amerise, Ilaria L., 2017. "Short-term load forecasting using a two-stage sarimax model," Energy, Elsevier, vol. 133(C), pages 108-114.
    8. Brabec, Marek & Konár, Ondrej & Pelikán, Emil & Malý, Marek, 2008. "A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers," International Journal of Forecasting, Elsevier, vol. 24(4), pages 659-678.
    9. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    10. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    11. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    12. Lu, Hongfang & Cheng, Feifei & Ma, Xin & Hu, Gang, 2020. "Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower," Energy, Elsevier, vol. 203(C).
    13. Deetman, Sebastiaan & Hof, Andries F. & Pfluger, Benjamin & van Vuuren, Detlef P. & Girod, Bastien & van Ruijven, Bas J., 2013. "Deep greenhouse gas emission reductions in Europe: Exploring different options," Energy Policy, Elsevier, vol. 55(C), pages 152-164.
    14. Ediger, Volkan S. & Akar, Sertac, 2007. "ARIMA forecasting of primary energy demand by fuel in Turkey," Energy Policy, Elsevier, vol. 35(3), pages 1701-1708, March.
    15. Sanchez-Ubeda, Eugenio Fco. & Berzosa, Ana, 2007. "Modeling and forecasting industrial end-use natural gas consumption," Energy Economics, Elsevier, vol. 29(4), pages 710-742, July.
    16. Kinateder, Harald & Campbell, Ross & Choudhury, Tonmoy, 2021. "Safe haven in GFC versus COVID-19: 100 turbulent days in the financial markets," Finance Research Letters, Elsevier, vol. 43(C).
    17. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    18. Chen, Ying & Xu, Xiuqin & Koch, Thorsten, 2020. "Day-ahead high-resolution forecasting of natural gas demand and supply in Germany with a hybrid model," Applied Energy, Elsevier, vol. 262(C).
    19. Perron, Pierre, 1990. "Testing for a Unit Root in a Time Series with a Changing Mean," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 153-162, April.
    20. Karadede, Yusuf & Ozdemir, Gultekin & Aydemir, Erdal, 2017. "Breeder hybrid algorithm approach for natural gas demand forecasting model," Energy, Elsevier, vol. 141(C), pages 1269-1284.
    21. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    22. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
    23. Karakurt, Izzet, 2021. "Modelling and forecasting the oil consumptions of the BRICS-T countries," Energy, Elsevier, vol. 220(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sebastian Majewski & Urszula Mentel & Raufhon Salahodjaev & Marek Cierpiał-Wolan, 2022. "Electricity Consumption and Economic Growth: Evidence from South Asian Countries," Energies, MDPI, vol. 15(4), pages 1-10, February.
    2. Duraisamy Pachiyappan & Yasmeen Ansari & Md Shabbir Alam & Prabha Thoudam & Kuppusamy Alagirisamy & Palanisamy Manigandan, 2021. "Short and Long-Run Causal Effects of CO 2 Emissions, Energy Use, GDP and Population Growth: Evidence from India Using the ARDL and VECM Approaches," Energies, MDPI, vol. 14(24), pages 1-17, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Potočnik, Primož & Soldo, Božidar & Šimunović, Goran & Šarić, Tomislav & Jeromen, Andrej & Govekar, Edvard, 2014. "Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia," Applied Energy, Elsevier, vol. 129(C), pages 94-103.
    2. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
    3. Qiao, Weibiao & Liu, Wei & Liu, Enbin, 2021. "A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of U.S," Energy, Elsevier, vol. 235(C).
    4. Jürgen Wolters & Uwe Hassler, 2006. "Unit Root Testing," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 4, pages 41-56, Springer.
    5. Ayşe Özmen, 2023. "Sparse regression modeling for short- and long‐term natural gas demand prediction," Annals of Operations Research, Springer, vol. 322(2), pages 921-946, March.
    6. Cem Saatçioðlu & Levent Korap, 2007. "Turkish Money Demand, Revisited: Some Implications For Inflation And Currency Substitution Under Structural Breaks," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 21(1+2), pages 107-124.
    7. Wang, Dabin & Tomek, William G., 2004. "Commodity Prices And Unit Root Tests," 2004 Annual meeting, August 1-4, Denver, CO 20141, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    8. Henry, Olan T. & Shields, Kalvinder, 2004. "Is there a unit root in inflation?," Journal of Macroeconomics, Elsevier, vol. 26(3), pages 481-500, September.
    9. Isabel Cortés-Jiménez & Manuel Artís, 2005. "The role of the tourism sector in economic development - Lessons from the Spanish experience," ERSA conference papers ersa05p488, European Regional Science Association.
    10. Francis Ahking, 2003. "Efficient unit root tests of real exchange rates in the post-Bretton Woods era," Economics Bulletin, AccessEcon, vol. 6(7), pages 1-12.
    11. Derek Bond & Michael J. Harrison & Edward J. O'Brien, 2005. "Testing for Long Memory and Nonlinear Time Series: A Demand for Money Study," Trinity Economics Papers tep20021, Trinity College Dublin, Department of Economics.
    12. Paroissien, Emmanuel, 2020. "Forecasting bulk prices of Bordeaux wines using leading indicators," International Journal of Forecasting, Elsevier, vol. 36(2), pages 292-309.
    13. Kyritsis, Evangelos & Serletis, Apostolos, 2018. "The zero lower bound and market spillovers: Evidence from the G7 and Norway," Research in International Business and Finance, Elsevier, vol. 44(C), pages 100-123.
    14. Claude Diebolt & Cédric Doliger, 2005. "Becker vs. Easterlin. Education, Fertility and Growth in France after World War II," Working Papers 05-03, Association Française de Cliométrie (AFC).
    15. Johannes W. Fedderke, 2022. "Identifying steady‐state growth and inflation in the South African economy, 1960–2020," South African Journal of Economics, Economic Society of South Africa, vol. 90(3), pages 279-300, September.
    16. Singh, Tarlok, 2014. "On the regime-switching and asymmetric dynamics of economic growth in the OECD countries," Research in Economics, Elsevier, vol. 68(2), pages 169-192.
    17. Nielsen, Morten, 2008. "A Powerful Tuning Parameter Free Test of the Autoregressive Unit Root Hypothesis," Working Papers 08-05, Cornell University, Center for Analytic Economics.
    18. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    19. Uddin, Gazi Salah & Shahbaz, Muhammad & Arouri, Mohamed & Teulon, Frédéric, 2014. "Financial development and poverty reduction nexus: A cointegration and causality analysis in Bangladesh," Economic Modelling, Elsevier, vol. 36(C), pages 405-412.
    20. Nicholas Apergis & Stephen M. Miller, 2007. "Total Factor Productivity and Monetary Policy: Evidence from Conditional Volatility," International Finance, Wiley Blackwell, vol. 10(2), pages 131-152, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6021-:d:640419. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.