1,068
Views
1
CrossRef citations to date
0
Altmetric
GENERAL & APPLIED ECONOMICS

Employing a generalized reduced gradient algorithm method to form combinations of steel price forecasts generated separately by ARIMA-TF and ANN models

ORCID Icon & ORCID Icon
Article: 2169997 | Received 04 Sep 2022, Accepted 15 Jan 2023, Published online: 12 Feb 2023

References

  • Adli, K. A., & Sener, U. (2021). Forecasting of the U.S. Steel prices with LVAR and VEC models. Business and Economics Research Journal, 12(3), 509–25. https://doi.org/10.20409/berj.2021.335
  • Ajupov, A. A., Kurilova A. A, and Ivanov, D. U. (2015). Application of Financial Engineering Instruments in the Russian Automotive Industry. ASS, 11(11). https://doi.org/10.5539/ass.v11n11p162
  • Aksu, C., & Gunter, S. I. (1992). An empirical analysis of the accuracy of SA, OLS, ERLS and NRLS combination forecasts. International Journal of Forecasting, 8. https://doi.org/10.1016/0169-2070(92)90005-T
  • Armstrong, J. S. (2001). Principles of forecasting A handbook for researchers and practitioners. Springer Science.
  • Bates, J. M., & Granger, C. W. J. (1969). The combination of forecasts. Operational Research Society, 20(4), 451–468. https://doi.org/10.1057/jors.1969.103
  • Bischoff, C. W. (1989). The combination of macroeconomic forecasts. Journal of Forecasting, Vat, 8(3), 293–314. https://doi.org/10.1002/for.3980080312
  • Bodnar, GM et al. (1995). Wharton survey of derivatives usage by US non-financial firms.Financial management,24(2), 104–114.
  • Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control (1st) ed.). Holden-Day.
  • Box, G. E. P., Jenkins, G. M., & Ljung, G. M. (2016). Time series analysis forecasting and control (5th) ed.). Wiley.
  • Chen, D. (2011). Chinese automobile demand prediction based on ARIMA model. Proceedings - 2011 4th International Conference on Biomedical Engineering and Informatics, BMEI 2011, 4, 2197–2201. https://doi.org/10.1109/BMEI.2011.6098744
  • Chen, Y., Zhao, H., & Yu, L. (2010). Demand forecasting in automotive aftermarket based on ARMA model. 2010 International Conference on Management and Service Science, MASS 2010. https://doi.org/10.1109/ICMSS.2010.5577867
  • Chou, M. T. (2013). Review of economics & finance, 3, 90–98.
  • Clemen, R. T. (1986). Linear constraints and the efficiency of combined forecasts. Journal of Forecasting, 5(1), 8–31. https://doi.org/10.1002/for.3980050104
  • Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5(4), 559–583. https://doi.org/10.1016/0169-2070(89)90012-5
  • Cooper, J. P., & Nelson, C. R. (1975). The Ex Ante prediction performance of the St. Louis and FRB-MIT-PENN econometric models and some results on composite predictors. Journal of Money, Credit and Banking, 7(1), 1. https://doi.org/10.2307/1991250
  • Diebold, F. X. (2015). Comparing predictive accuracy, twenty years later: A personal perspective on the use and abuse of Diebold–Mariano tests. Journal of Business and Economic Statistics, 33(1), 37–41. https://doi.org/10.1080/07350015.2014.983236
  • Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 134–144. https://doi.org/10.1016/0169-2070(88)
  • Diebold, F. X., & Pauly, P. (1987). Structural change and the combination of forecasts. Journal of Forecasting, 6. https://doi.org/10.1002/for.3980060103
  • Drought, S., & McDonald, C. (2011). Forecasting house price inflation: A model combination approach. Reserve Bank of New Zealand Discussion, October, 1–23. http://nzae.org.nz/wp-content/uploads/2011/08/Drought_and_McDonald__Forecasting_House_Price_Inflation.pdf
  • Fair, R. C., & Shiller, R. J. (1990). Comparing information in forecasts from econometric models. The American Economic Review, 80(3), 375–389. https://www.jstor.org/stable/2006672
  • Granger, C., & Newbold, P. (1975). Economic Forecasting-the atheist's viewpoint. Modelling the economy, 131–147.
  • Granger, C. W. J., & Ramanathan, R. (1984). Improved methods of combining forecasts. Journal of Forecasting, 3(2), 197–204. https://doi.org/10.1002/for.3980030207
  • Guerard, J. B. (1987). Linear constraints, robust weighing and efficient composite modelling. Journal of Forecasting, 6(3), 193–199. https://doi.org/10.1002/for.3980060305
  • Gunter, S. I. (1992). Nonnegativity restricted least squares combinations. International Journal of Forecasting, 8, https://doi.org/10.1016/0169-2070(92)90006-U
  • Gunter, S. I., & Aksu, C. (1997). The usefulness of heuristic N(E)RLS algorithms for combining forecasts. Journal of Forecasting, 16(6), 439–462. https://doi.org/10.1002/(sici)1099-131x(199711)16:6<439::AID-FOR624>3.0.CO;2-8
  • Hakim, D. (2003). Steel Supplier Is Threatening To Terminate G.M. Shipments. New York Times.
  • Hanke, J. E., & Wichern, D. W. (2014). Business forecasting (9th) ed.). Pearson.
  • Harvey, D., Leybourne, S., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2), 281–291. https://doi.org/10.1016/S0169-2070(96)00719-4
  • Kapl, M., & Müller, W. G. (2010). Prediction of steel prices: A comparison between a conventional regression model and MSSA. Statistics and Its Interface, 3(3), 369–375. https://doi.org/10.4310/sii.2010.v3.n3.a10
  • Kim, S., Choi, C.-Y., Shahandashti, M., & Ryu, K. R. (2022). Improving Accuracy in Predicting City-Level Construction Cost Indices by Combining Linear ARIMA and Nonlinear ANNs. Journal of Management in Engineering, 38(2), 2. https://doi.org/10.1061/(asce)me.1943-5479.0001008
  • Lasdon, L., Fox, R., & Ratner, M. (1974). Nonlinear optimization using the generalized reduced gradient method. RAIRO - Operations Research - Recherche Opérationnelle, 3(8), 73–103. http://www.numdam.org/item?id=RO_1974__8_3_73_0
  • Lasdon, L. S., Waren, A. D., Jain, A., & Ratner, M. (1978). Design and testing of a generalized reduced gradient code for nonlinear programming. ACM Transactions on Mathematical Software (TOMS), 4(1), 34–50. https://doi.org/10.1145/355769.355773
  • Liebman, B. H. (2006). Safeguards, China, and the price of steel. Review of World Economics, 142(2), 354–373. https://doi.org/10.1007/s10290-006-0071-y
  • Liu X. (2020). Does Industrial Agglomeration Affect the Accuracy of Analysts’ Earnings Forecasts?. AJIBM, 10(05), 900–914. https://doi.org/10.4236/ajibm.2020.105060
  • Liu, Z., Wang, Y., Zhu, S., & Zhang, B. (2015). Steel prices index prediction in china based on BP neural network. Springer, 603–608. https://doi.org/10.1007/978-3-662-43871-8
  • Lobo, G. (1991). Alternative methods of combining security analysts’ and statistical forecasts of annual corporate earnings. International Journal of Forecasting, 7(1), 57–63. https://doi.org/10.1016/0169-2070(91)
  • Makridakis, S. G., Parzen, E., Fildes, R., & Andersen, A. (1984). The forecasting accuracy of major time series methods. John Wiley & Sons.
  • Makridakis, S., & Wheelwright SC, H. R. (1997). Forecasting methods and applications. In Forecasting methods and applications (3rd) ed.), (pp. 632). John Wiley & Sons.
  • Malanichev, A. G., & Vorobyev, P. V. (2011). Forecast of global steel prices. Studies on Russian Economic Development, 22(3), 304–311. https://doi.org/10.1134/S1075700711030105
  • Mancke, R. (1968). The Determinants of Steel Prices in the U.S.: 1947-65. The Journal of Industrial Economics, 16(2), 147. https://doi.org/10.2307/2097798
  • Mathews, R. G. (2011). Steel-Price Increases Creep Into Supply Chain. The Wall Street Journal.
  • Mir, M., Kabir, H. M. D., Nasirzadeh, F., & Khosravi, A. (2021). Neural network-based interval forecasting of construction material prices. Journal of Building Engineering, 39(February), 102288. https://doi.org/10.1016/j.jobe.2021.102288
  • Nelson, C. R. (1972). The prediction performance of the FRB-MIT-PENN model of the U.S. economy. American Economic Association, 62, 5. https://www.jstor.org/stable/1815208
  • Nowotarski, J., Raviv, E., Trück, S., & Weron, R. (2014). An empirical comparison of alternative schemes for combining electricity spot price forecasts. Energy Economics, 46, 395–412. https://doi.org/10.1016/j.eneco.2014.07.014
  • Rapach, D. E., & Strauss, J. K. (2009). Differences in housing price forecastability across US states. International Journal of Forecasting, 25(2), 351–372. https://doi.org/10.1016/j.ijforecast.2009.01.009
  • Şener, U. (2015). Tahmin metodolojisi ve tahmin yöntemi seçimi. Beykoz Akademi Dergisi, 3(1), 85–98. https://dergipark.org.tr/tr/pub/beykozad/issue/52157/682081
  • Shivashankar, M., Pandey, M., & Zakwan, M. (2022). Estimation of settling velocity using generalized reduced gradient (GRG) and hybrid generalized reduced gradient–genetic algorithm (hybrid GRG-GA). Acta Geophysica, 0123456789. https://doi.org/10.1007/s11600-021-00706-2
  • Stock, J. H., & Watson, M. W. (2004). Combination forecasts of output growth in a seven-country data set. Journal of Forecasting, 23(6), 405–430. https://doi.org/10.1002/for.928
  • Terregrossa, S. J. (2005). On the efficacy of constraints on the linear combination forecast model. Applied Economics Letters, 12(1), 19–28. https://doi.org/10.1080/1350485042000307062
  • Terregrossa, S. J., & Ibadi, M. H. (2021). Combining housing price forecasts generated separately by hedonic and artificial neural network models. Asian Journal of Economics, Business and Accounting, 21(1), 130–148. https://doi.org/10.9734/ajeba/2021/v21i130345
  • Torres-Pruñonosa, J., García-Estévez, P., Raya, J. M., & Prado-Román, C. (2022). How on earth did Spanish banking sell the housing stock? SAGE Open, 12(1), 1. https://doi.org/10.1177/21582440221079916
  • Turcic, D., Kouvelis, P., & Bolandifar, E. (2014). Hedging Commodity Procurement in a Bilateral Supply Chain. M&SOM, 17(2), 221–235. https://doi.org/10.1287/msom.2014.0514
  • Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83. https://doi.org/10.2307/3001968
  • Yuzefovych, I. (2006). Ukrainian industry in transition: Steel price determination model. National University Kyiv-Mohyla Academy.
  • Zhang, G.P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175.
  • Zhang, M. (2008). Artificial higher order neural networks for economics and business. Artificial Higher Order Neural Networks for Economics and Business. https://doi.org/10.4018/978-1-59904-897-0
  • Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35–62. https://doi.org/10.1016/S0169-2070(97)