References
Ahn, J., Park, C.-G., & Park, C. (2017). Pass-through of imported input prices to domestic producer prices: Evidence from sector-level data. The B.E. Journal of Macroeconomics, 17(2). https://doi.org/10.1515/bejm-2016-0034
Search via ReFindit
Alcalde, R., Urda, D., de Armiño, C. A., García, S., Manzanedo, M., & Herrero, Á. (2023). Non-linear neural models to predict HRC steel price in Spain. In P. García Bringas, H. Pérez García, F. J. Martinez-de-Pison, J. R. Villar Flecha, A. Troncoso Lora, E. A. de la Cal, Á. Herrero, F. Martínez Álvarez, G. Psaila, H. Quintián, & E. S. Corchado Rodriguez (Ed.), 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) (Roč. 531, s. 186–194). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-18050-7_18
Search via ReFindit
Baláž, P., & Bayer, J. (2019). Energy prices and their impact on the competitiveness of the EU Steel Industry. Prague Economic Papers, 28(5), 547–566. https://doi.org/10.18267/j.pep.715
Search via ReFindit
Behun M., Gavurova B., Tkacova A.,& Kotaskova A. (2018). The impact of the manufacturing industry on the economic cycle of European union countries. Journal of Competitiveness, 10(1), 23-39. https://doi.org/10.7441/joc.2018.01.02
Search via ReFindit
Bilan, Y., Gavurova, B., Stanislaw, G., & Tkacova, A. (2017). The Composite Coincident Indicator (CCI) for Business Cycles. Acta Polytechnica Hungarica, 14(7), 71–90. SI. . https://doi.org/10.12700/aph.14.7.2017.7.5
Search via ReFindit
Carrasco, R., Vargas, M., Soto, I., Fuertes, G., & Alfaro, M. (2015). Copper metal price using chaotic time series forecasting. IEEE Latin America Transactions, 13(6), 1961–1965. https://doi.org/10.1109/tla.2015.7164223
Search via ReFindit
Cerasa, A., & Buscaglia, D. (2019). A hedonic model of import steel prices: Is the EU market integrated? Resources Policy, 61, 241–249. https://doi.org/10.1016/j.resourpol.2019.01.018
Search via ReFindit
Česká spořitelna. (2020). Vývoj cen oceli. https://www.csas.cz/content/dam/cz/csas/www_csas_cz/Dokumenty-korporat/Dokumenty/Analytici/vyvoj-cen-oceli-%202020-04.pdf
Search via ReFindit
Česká spořitelna. (2022). Vývoj cen oceli. https://www.csas.cz/cs/research/analyza/cz/SR271417
Search via ReFindit
Český statistický úřad. (2021). Statistická ročenka Jihočeského kraje 2021. https://www.czso.cz/csu/czso/13-prumysl-hd544mw41x
Search via ReFindit
Cetin, K., Aksoy, S., & Iseri, I. (2019). Steel price forecasting using long short-term memory network model. 2019 4th International Conference on Computer Science and Engineering (UBMK), 612–617. https://doi.org/10.1109/ubmk.2019.8907015
Search via ReFindit
Chen, Y. & Yang, S. (2022). How does the reform in pricing mechanism affect the world's iron ore price. Romanian journal of economic forecasting. Institute for Economic Forecasting, 2022, ročník: 25, 83-103. https://ipe.ro/rjef.htm
Search via ReFindit
Chen, Y., & Yang, S. (2021). Time-varying effect of international iron ore price on China’s inflation: A complete price chain with TVP-Svar-SV model. Resources Policy, 73, 102200. https://doi.org/10.1016/j.resourpol.2021.102200
Search via ReFindit
Credendo. (2021). Ocelářství: Ceny oceli v důsledku zvýšené čísnké poptávky prudsce stouply. https://credendo.com/cs/knowledge-hub/ocelarstvi-ceny-oceli-v-dusledku-zvysene-cinske-poptavky-prudce-stouply
Search via ReFindit
Echo24. (2021). Cena železa rekordně roste a je ho nedostatek. Zdražuje to Výstavbu Bydlení. Echo24.cz. https://echo24.cz/a/S9WZJ/cena-zeleza-rekordne-roste-a-je-ho-nedostatek-zdrazuje-to-vystavbu-bydleni
Search via ReFindit
European Central Bank. (2021). Macroeconomic projections. https://www.ecb.europa.eu/pub/projections/html/all-releases.en.html
Search via ReFindit
Faghih, S. A., & Kashani, H. (2018). Forecasting construction material prices using vector error correction model. Journal of Construction Engineering and Management, 144(8), 04018075. co.1943-7862.0001528 https://doi.org/10.1061/(asce)
Search via ReFindit
Fischer, D. (2021). Korrelieren Stahlpreise und Stahlbaupreise? Eine statistische untersuchung. Stahlbau, 90(7), 542–547. https://doi.org/10.1002/stab.202100041
Search via ReFindit
Fiszeder, P., & Małecka, M. (2022). Forecasting volatility during the outbreak of Russian invasion of Ukraine: application to commodities, stock indices, currencies, and cryptocurrencies. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(4), 939–967. https://doi.org/10.24136/eq.2022.032
Search via ReFindit
García-Gonzalo, E., García Nieto, P. J., Gracia Rodríguez, J., Sánchez Lasheras, F., & Fidalgo Valverde, G. (2022). A support vector regression model for time series forecasting of the Comex Copper Spot Price. Logic Journal of the IGPL, 31(4), 775–784. https://doi.org/10.1093/jigpal/jzac039
Search via ReFindit
Gavurová, B., Behúnová, A., Tkáčová, A., & Peržeľová, I. (2017). The mining industry and its position in the economic cycle of the EU countries. Acta Montanistica Slovaca, 22(3). https://doi.org/10.30686/1609-9192-2020-3-98-104
Search via ReFindit
Gomwe, C., & Li, Y. (2019). Iron ore price and the aud exchange rate: A Markov approach. The Journal of International Trade & Economic Development, 29(2), 147–162. https://doi.org/10.1080/09638199.2019.1655087
Search via ReFindit
Hančlová, J., Zapletal, F., & Šmíd, M. (2020). On interaction between carbon spot prices and Czech steel industry. Carbon Management, 11(2), 121–137. https://doi.org/10.1080/17583004.2020.1712262
Search via ReFindit
Investing.com. (2023). Energy Futures Prices. https://www.investing.com/commodities/energy
Search via ReFindit
Investing.com(b). (2023). Currencies. https://www.investing.com/currencies/cny-usd-historical-data
Search via ReFindit
Jeremić, D., Stanojević, S., Mihajlović, M., Ivanova, B., Kostić, R., & Marjanović, N. (2022). Correlation Analysis of the crude oil, gold and steel prices for the purpose of agricultural development. Ekonomika Poljoprivrede, 69(3), 713–731. https://doi.org/10.5937/ekopolj2203713j
Search via ReFindit
Jian Ming, Nailian Hu, & Jinhai Sun. (2016). Study of iron concentrate price forecasting models based on data mining. 2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), 140–145. https://doi.org/10.1109/ICCCBDA.2016.7529548
Search via ReFindit
Kahraman, E., & Akay, O. (2022). Comparison of exponential smoothing methods in forecasting global prices of Main Metals. Mineral Economics, 36(3), 427–435. https://doi.org/10.1007/s13563-022-00354-y
Search via ReFindit
Kim, Y., Ghosh, A., Topal, E., & Chang, P. (2022). Relationship of iron ore price with other major commodity prices. Mineral Economics, 35(2), 295–307. https://doi.org/10.1007/s13563-022-00301-x
Search via ReFindit
Kolkova, A. ., & Rozehnal, P. (2022). Hybrid demand forecasting models: pre-pandemic and pandemic use studies. Equilibrium. Quarterly Journal of Economics and Economic Policy, 17(3), 699–725. https://doi.org/10.24136/eq.2022.024
Search via ReFindit
Kolková, A., & Ključnikov, A. (2021). Demand forecasting: an alternative approach based on technical indicator Pbands. Oeconomia Copernicana, 12(4), 1063–1094. https://doi.org/10.24136/oc.2021.035
Search via ReFindit
Landmesser, J. (2021). The use of the dynamic time warping (DTW) method to describe the COVID-19 dynamics in Poland. Oeconomia Copernicana, 12(3), 539–556. https://doi.org/10.24136/oc.2021.018
Search via ReFindit
Lasheras, F. S., Nieto, P. J., García-Gonzalo, E., Valverde, G. F., & Krzemień, A. (2021). Time series forecasting of gold prices with the help of its decomposition and multivariate adaptive regression splines. 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021), 135–144. Springer International Publishing. https://doi.org/10.1007/978-3-030-87869-6_13
Search via ReFindit
Lee, C., Won, J., & Lee, E.-B. (2019). Method for predicting raw material prices for product production over long periods. Journal of Construction Engineering and Management, 145(1), 05018017. co.1943-7862.0001586 https://doi.org/10.1061/(asce)
Search via ReFindit
Li, D., Moghaddam, M. R., Monjezi, M., Jahed Armaghani, D., & Mehrdanesh, A. (2020). Development of a group method of data handling technique to forecast iron ore price. Applied Sciences, 10(7), 2364. https://doi.org/10.3390/app10072364
Search via ReFindit
Liu, Z., Zhu, S., Wang, Y., Zhang, B., & Wei, L. (2015). Thread steel price index prediction in China based on Arima model. LISS 2014, 609–614. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-43871-8_88
Search via ReFindit
Lv, J., Tang, W., & Hosseinzadeh, H. (2022). Developed multiple-layer perceptron neural network based on developed search and Rescue Optimizer to predict iron ore price volatility: A case study. ISA Transactions, 130, 420–432. https://doi.org/10.1016/j.isatra.2022.04.025
Search via ReFindit
Ma, Y. (2021). Dynamic spillovers and dependencies between iron ore prices, industry bond yields, and steel prices. Resources Policy, 74, 102430. https://doi.org/10.1016/j.resourpol.2021.102430
Search via ReFindit
Ma, Y., & Wang, J. (2019). Co-movement between oil, gas, coal, and iron ore prices, the Australian dollar, and the Chinese RMB Exchange rates: A copula approach. Resources Policy, 63, 101471. https://doi.org/10.1016/j.resourpol.2019.101471
Search via ReFindit
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, 102288. https://doi.org/10.1016/j.jobe.2021.102288
Search via ReFindit
Nicola, F. de, De Pace, P., & Hernandez, M. A. (2016). Co-movement of major energy, agricultural, and Food Commodity price returns: A time-series assessment. Energy Economics, 57, 28–41. https://doi.org/10.1016/j.eneco.2016.04.012
Search via ReFindit
OEC. (2022). The Observatory of Economic Complexity, Iron Ore and Concentrates, Overview and Histrical data from 2020. https://oec.world/en/profile/hs/iron-ore
Search via ReFindit
Qi, Y., Li, H., Liu, Y., Feng, S., Li, Y., & Guo, S. (2020). Granger causality transmission mechanism of steel product prices under multiple scales - The industrial chain perspective. Resources Policy, 67, 101674. https://doi.org/10.1016/j.resourpol.2020.101674
Search via ReFindit
Rokicki, T. (2019). Foreign trade in iron ore in EU countries. METAL Conference Proeedings. https://doi.org/10.37904/metal.2019.984
Search via ReFindit
Shafiullah, M., Chaudhry, S. M., Shahbaz, M., & Reboredo, J. C. (2020). Quantile causality and dependence between crude oil and precious metal prices. International Journal of Finance & Economics, 26(4), 6264–6280. https://doi.org/10.1002/ijfe.2119
Search via ReFindit
Simionescu, M., & Gavurova, B. (2023). Financial development and natural resources for the top five gas exporters. Heliyon, 9(9). https://doi.org/10.1016/j.heliyon.2023.e20273
Search via ReFindit
Simionescu, M., Strielkowski, W., & Gavurova, B. (2022). Could quality of governance influence pollution? Evidence from the revised Environmental Kuznets Curve in Central and Eastern European countries. Energy Reports, 8, 809-819. https://doi.org/10.1016/j.egyr.2021.12.031
Search via ReFindit
Simionescu, M., Szeles, M. R., Gavurova, B., & Mentel, U. (2021). The impact of quality of governance, renewable energy and foreign direct investment on sustainable development in CEE countries. Frontiers in Environmental Science, 9, 765927. https://doi.org/10.3389/fenvs.2021.765927
Search via ReFindit
Tkacova, A., & Gavurova, B. (2023). Economic sentiment indicators and their prediction capabilities in business cycles of EU countries. Oeconomia Copernicana, 14(3), 977–1008. https://doi.org/10.24136/oc.2023.029
Search via ReFindit
Tradingeconomics.com. (2023). Price of HRC steel. https://tradingeconomics.com/commodity/hrc-steel
Search via ReFindit
Tradingeconomics.com. (2023). Price of steel rebar. https://tradingeconomics.com/commodity/steel
Search via ReFindit
Tsiakas, I., & Zhang, H. (2021). Economic Fundamentals and the long-run correlation between exchange rates and commodities. Global Finance Journal, 49, 100649. https://doi.org/10.1016/j.gfj.2021.100649
Search via ReFindit
Tuo, J., & Zhang, F. (2020). Modelling the iron ore price index: A new perspective from a hybrid data reconstructed EEMD-GORU model. Journal of Management Science and Engineering, 5(3), 212–225. https://doi.org/10.1016/j.jmse.2020.08.003
Search via ReFindit
Vochozka, M., Kalinová, E., Gao, P. & Smolíková, L. (2021). Development of copper price from July 1959 and predicted development till the end of year 2022. Acta Montanistica Slovaca, (26), 262–280. https://doi.org/10.46544/ams.v26i2.07
Search via ReFindit
Wårell, L. (2018). An analysis of iron ore prices during the latest commodity boom. Mineral Economics, 31(1–2), 203–216. https://doi.org/10.1007/s13563-018-0150-2
Search via ReFindit
Xu, X., & Zhang, Y. (2022). Steel price index forecasting through Neural Networks: The composite index, Long Products, flat products, and rolled products. Mineral Economics. https://doi.org/10.1007/s13563-022-00357-9
Search via ReFindit
Zhang, Q. (2015). Research on prediction of architectural engineering cost based on the time series method. Proceedings of the 2015 3rd International Conference on Machinery, Materials and Information Technology Applications Qingdao, China. https://doi.org/10.2991/icmmita-15.2015.122
Search via ReFindit
Zola, P., & Carpita, M. (2016). Forecasting the steel product prices with the ARIMA model. Statistica & Applicazioni: XIV, 1, 2016, XIV. https://doi.org/10.1400/250432
Search via ReFindit
RSS 1.0
