Monika Papież , Sławomir Śmiech

(Polish) PDF


The article presents the analysis of the relations between the demand for steel products and the prices of steel and raw materials (coking coal) on the European market based on the monthly data in the period 2003-2011. The analysis of those relations was conducted with the use of Structural Vector Error Correction Model (SVECM), which allowed to determine the impact of the supply of raw materials and the demand for steel products on the prices of steel products. The results obtained indicate that steel market is in long run equilibrium. The price of coking coal is the dominant variable in the system, and the prices of crude steel depend on the prices of raw materials and the demand for steel products. The price of steel is not the Granger cause for the remaining variables. As expected, the system reacts both to the increase in the demand for steel products and the increase in the prices of raw materials. The forecasts obtained using the model are valid enough and can be treated as reference points by the participants of the steel market.


causality, SVECM model, steel market, coke and steel prices


[1] Blanchard O., Quah D., (1989), The Dynamic Effects of Aggregate Demand and Supply Disturbances, American Economic Review, 79, 655-673.

[2] Charemza W.W., Deadman D.F., (1997), Nowa ekonometria, PWE, Warszawa.

[3] Coke Market Report – Resource-Net,

[4] Crude Steel Quarterly Industry & Market Outlook, (2011),

[5] Dickey D.A., Fuller W.A., (1981), Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root, Econometrica, 49, 1057-1072.

[6] Doornik J.A., Hansen H., (1994), A practical test for univariate and multivariate normality, Discussion paper, Nuffield College, University of Oxford.

[7] Dufour J.M., Pelletier D., Renault E., (2006), Short Run and Long Run Causality in Time Series: Inference, Journal of Econometrics, 132 (2), 337-362.

[8] Ghosh S., (2006), Steel Consumption and Economic Growth: Evidence from India, Resources Policy, 31, 7-11.

[9] Granger C.W.J., Weiss, (1983), Time Series Analysis of Error-Correcting Models, Studies in Econometrics, Time Series, and Multivariate Statistics, New York, Academic Press, 255-278.

[10] Granger C.W.J., (1969), Investigating Causal Relations by Econometric Models and Cross-Spectral Methods, Econometrica, 37 (3), 424-438.

[11] Hsiao C., (1982), Autoregressive Modelling and Causal Ordering of Economic Variables, Journal of Economic Dynamic and Control, 4, 243-259.

[12] Johansen S., Juselius K., (1990), Maximum Likelihood Estimation and Inference on Cointegration– with Application to the Demand for Money, Oxford Bulletin of Economic and Statistics, 52,169-210.

[13] Johansen S., (1995), Likelihood-Based Inference in Cointegrated Vector Autoregressive Models,Oxford University Press, Oxford.

[14] King R.G., Plosser C.I., Stock J.H., Watson M.W., (1991), Stochastic Trends and Economic Fluctuations,American Economic Review, 81, 819-840.

[15] Kwang-Sook H., (2011), Steel Consumption and Economic Growth in Korea: Long-Term and Short-Term Evidence, Resources Policy, 36, 107-113.

[16] Ljung G.M., Box G.E.P., (1978), On a Measure of Lack of Fit in Time Series Models, Biometrika, 65, 297–303.

[17] Lutkepohl H., (2007), New Introduction to Multiple Time Series Analysis, corr. 2nd print, Springer, Berlin.

[18] Lutkepohl H, Kratzig M. (red.), (2004), Applied Time Series Econometrics, Cambridge University Press.

[19] Osinska M., (2006), Ekonometria finansowa, Polskie Wydawnictwo Ekonomiczne.

[20] Osinska M., (2008), Ekonometryczna analiza zaleznosci przyczynowych, Wydawnictwo Naukowe UMK, Torun.

[21] Osinska M., (2009), Analiza przyczynowosci w długim i krótkim okresie w modelu popytu na pieniadz, Acta Universitatis Nicolai Copernici, Ekonomia XXXIX, 40-50.

[22] Ozga-Blaschke U., (2008), Analiza sytuacji na swiatowych rynkach stali oraz prognozy w zakresie zmian popytu i podazy, Czasopismo Techniczne, nr 134-137, 1-10.

[23] Ozga-Blaschke U., (2009), Wpływ kryzysu gospodarczego na rynki stali, wegla koksowego i koksu, Przeglad Górniczy, 3-4, 1036-37.

[24] Papiez M., Smiech S., (2012), Analiza przyczynowosci na rynku koksu, wegla koksowego i stali w latach 2003-2010, A. Sagan (red.), Metody analizy danych, Zeszyt Naukowy Uniwersytet Ekonomiczny w Krakowie, 878, 57-71.

[25] Papiez M., Smiech S., (2011a), The Analysis and Forecasting of Coke Prices. Econometrics 32 /red. Dittmann P., Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu 196, 213-220.

[26] Papiez M., Smiech S., (2011b), The Analysis of Relations Between Primary Fuel Prices on the European Market in the Period 2001-2011, Rynek Energii 5(96), 139-144.

[27] Rebiasz B., (2006), Polish Steel Consumption 1974-2008, Resources Policy, 31, 37-49.

[28] Smiech S., Papiez M., Fijorek K., Causality on the Steam Coal Market. Energy Sources, Part B: Economics, Planning, and Policy, DOI:10.1080/15567249.2011.627909.

[29] Toda H.Y., Yamamoto T., (1995), Statistical Inference in Vector Autoregressions with Possibly Integrated Processes, Journal of Econometrics, 66, 225-250.

[30] Wold, H., (1960), A Generalization of Causal Chain Models, Econometrica, 28, 443–463.



[33] Wydymus S., Papiez M., Smiech S., Zysk W., Jasko P., (2010), Modelowanie i prognozowanie cen koksu na rynkach swiatowych – studium przypadku, [w:] Bezposrednie inwestycje zagraniczne jako czynnik konkurencyjnosci handlu zagranicznego (red. Maciejewski M. Wydymus S.), 241-262.

Back to top
© 2019–2022 Copyright by Statistics Poland, some rights reserved. Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) Creative Commons — Attribution-ShareAlike 4.0 International — CC BY-SA 4.0