Form of Work
E-booki
(2)
IBUK Libra
(2)
Author
zbiorowa Praca
(2)
Year
2020 - 2022
(2)
Country
Poland
(2)
Language
English
(1)
Polish
(1)
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Central bank communication has become an essential part of monetary policy in recent years. Against the background of the zero interest rate policy, conventional monetary policy instruments have lost their effectiveness, and the management of market participants’ expectations by means of appropriate communication has become an essential part of monetary policy. In this paper a new model of the communication process between the central bank and financial market players is elaborated, in particular exploring the obstacles and hurdles in the transmission of information from sender to recipient, empirical data are also reported for several stages of the new model. For efficient expectation management, both the selection of information by the central bank as well as the transmission path of this information is important as indirect communication can lead to information distortions. Last but not least, the recipients of the information, as people with a limited capacity and variable readiness to absorb it, play a central role in determining what information actually reaches them. Appropriate communication that takes these obstacles and the recipients’ psychology into account can thus significantly increase the efficiency of monetary policy.
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This article focuses on the creation of artificial neural networks (ANN) and their use in predicting the volume of biofuel production in Poland on the basis of historical data. Artificial neural networks are extremely useful in predicting events in which it is difficult to find determinism and cause-effect relationships. For this purpose 30 artificial neural networks of different topology were created. The analysed artificial neural networks had: one or two layers, from 4 to 8 neurons on the first layer and 4 or 6 neurons on the second layer. Moreover, the effect of delayed inputs and the effect of learning set size on prediction quality were analysed. The quality of each structure was evaluated based on the coefficient of determination, mean error, and mean square error. The stability of prediction was evaluated based on the sample standard deviation of RMSE and MAE. All the presented ANN structures were simulated five times and the best individual results included in the tables. The best results were obtained for an artificial neural network with two layers, four neurons in each layer and one delay. Overall, the second layer increased the stability of the prediction.
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