IDEAS home Printed from https://ideas.repec.org/a/eee/ecoedu/v31y2012i4p452-462.html
   My bibliography  Save this article

Forecasting performance of grey prediction for education expenditure and school enrollment

Author

Listed:
  • Tang, Hui-Wen Vivian
  • Yin, Mu-Shang

Abstract

GM(1,1) and GM(1,1) rolling models derived from grey system theory were estimated using time-series data from projection studies by National Center for Education Statistics (NCES). An out-of-sample forecasting competition between the two grey prediction models and exponential smoothing used by NCES was conducted for education expenditure and school enrollment under the assumption that grey prediction was as promising as NCES's forecasting technique in dealing with univariate time-series data while some other determinants of the variables under examination were excluded. The purpose of this study, therefore, was to verify that the GM(1,1), and GM(1,1) rolling models would provide forecasts that were at least as accurate as the NCES's approach to extrapolating education expenditure and school enrollment. The findings revealed that the forecasting efficiency of GM(1,1) rolling model was superior to exponential smoothing and GM(1,1) model. The results can offer valuable insights and provide a basis for further research in model building for short-term estimation on educational statistics.

Suggested Citation

  • Tang, Hui-Wen Vivian & Yin, Mu-Shang, 2012. "Forecasting performance of grey prediction for education expenditure and school enrollment," Economics of Education Review, Elsevier, vol. 31(4), pages 452-462.
    • Handle: RePEc:eee:ecoedu:v:31:y:2012:i:4:p:452-462
    DOI: 10.1016/j.econedurev.2011.12.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S027277571100183X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econedurev.2011.12.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sylwester, Kevin, 2002. "Can education expenditures reduce income inequality?," Economics of Education Review, Elsevier, vol. 21(1), pages 43-52, February.
    2. Ram, Rati, 1995. "Public educational expenditures in the United States: An analytical comparison with other industrialized countries," Economics of Education Review, Elsevier, vol. 14(1), pages 53-61, March.
    3. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Fildes, Robert & Lusk, Edward J, 1984. "The choice of a forecasting model," Omega, Elsevier, vol. 12(5), pages 427-435.
    6. J. Scott Armstrong, 2005. "The Forecasting Canon: Nine Generalizations to Improve Forecast Accuracy," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 1, pages 29-35, June.
    7. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
    8. Ram, Rati, 1995. "Intercountry and intracountry inequalities in school enrollments: A broad international perspective," Economics of Education Review, Elsevier, vol. 14(4), pages 363-372, December.
    9. Viviana Fernandez, 2008. "Traditional versus novel forecasting techniques: how much do we gain?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 637-648.
    10. Baker, Bruce D. & Richards, Craig E., 1999. "A comparison of conventional linear regression methods and neural networks for forecasting educational spending," Economics of Education Review, Elsevier, vol. 18(4), pages 405-415, October.
    11. Kung, Ling-Ming & Yu, Shang-Wu, 2008. "Prediction of index futures returns and the analysis of financial spillovers--A comparison between GARCH and the grey theorem," European Journal of Operational Research, Elsevier, vol. 186(3), pages 1184-1200, May.
    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. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
    2. Wei Zhou & Demei Zhang, 2016. "An Improved Metabolism Grey Model for Predicting Small Samples with a Singular Datum and Its Application to Sulfur Dioxide Emissions in China," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-11, February.
    3. Natnael Nigussie Goshu & Surafel Luleseged Tilahun, 2016. "Grey theory to predict Ethiopian foreign currency exchange rate," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 2(2), pages 95-116.
    4. Zhou, Chenyu & Shen, Yun & Wu, Haixin & Wang, Jianhong, 2022. "Using fractional discrete Verhulst model to forecast Fujian's electricity consumption in China," Energy, Elsevier, vol. 255(C).

    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. Hayashi, Masayoshi, 2014. "Forecasting welfare caseloads: The case of the Japanese public assistance program," Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 105-114.
    2. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    3. Charles, Amelie & Darne, Olivier & Kim, Jae, 2016. "Stock Return Predictability: Evaluation based on Prediction Intervals," MPRA Paper 70143, University Library of Munich, Germany.
    4. Nieto, María Rosa & Carmona-Benítez, Rafael Bernardo, 2018. "ARIMA + GARCH + Bootstrap forecasting method applied to the airline industry," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 1-8.
    5. repec:zbw:bofitp:2015_012 is not listed on IDEAS
    6. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    7. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
    8. Zi‐Yi Guo, 2021. "Out‐of‐sample performance of bias‐corrected estimators for diffusion processes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 243-268, March.
    9. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    10. Sermpinis, Georgios & Stasinakis, Charalampos & Dunis, Christian, 2014. "Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 30(C), pages 21-54.
    11. Croonenbroeck, Carsten & Stadtmann, Georg, 2019. "Renewable generation forecast studies – Review and good practice guidance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 312-322.
    12. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
    13. Feuerriegel, Stefan & Gordon, Julius, 2019. "News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions," European Journal of Operational Research, Elsevier, vol. 272(1), pages 162-175.
    14. Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2016. "Evolving Fuzzy-GARCH Approach for Financial Volatility Modeling and Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 379-398, October.
    15. Kim, Jae H. & Wong, Kevin & Athanasopoulos, George & Liu, Shen, 2011. "Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals," International Journal of Forecasting, Elsevier, vol. 27(3), pages 887-901, July.
    16. Wang, Shuai & Yu, Lean & Tang, Ling & Wang, Shouyang, 2011. "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China," Energy, Elsevier, vol. 36(11), pages 6542-6554.
    17. Niu, Linlin & Xu, Xiu & Chen, Ying, 2015. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," SFB 649 Discussion Papers 2015-023, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    18. Bera, Soumitra Kumar, 2010. "Forecasting model of small scale industrial sector of West Bengal," MPRA Paper 28144, University Library of Munich, Germany.
    19. Strunz, Franziska & Gödl, Maximilian, 2023. "An Evaluation of Professional Forecasts for the German Economy," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277707, Verein für Socialpolitik / German Economic Association.
    20. Dumas, Jonathan & Wehenkel, Antoine & Lanaspeze, Damien & Cornélusse, Bertrand & Sutera, Antonio, 2022. "A deep generative model for probabilistic energy forecasting in power systems: normalizing flows," Applied Energy, Elsevier, vol. 305(C).
    21. Vortelinos, Dimitrios I., 2017. "Forecasting realized volatility: HAR against Principal Components Combining, neural networks and GARCH," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 824-839.

    More about this item

    Keywords

    Grey prediction; GM(1; 1); GM(1; 1) rolling; Education expenditure; School enrollment;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education

    Statistics

    Access and download statistics

    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:eee:ecoedu:v:31:y:2012:i:4:p:452-462. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/econedurev .

    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.