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This paper analyses the forecasting performance of monetary policy reaction functions using U.S. Federal Reserve's Greenbook real-time data. The results indicate that artificial neural networks are able to predict the nominal interest rate better than linear and nonlinearTaylor rule models as...
Persistent link: https://www.econbiz.de/10012256503
In diesem Artikel prognostizieren wir Insolvenzwahrscheinlichkeiten von kleinen und mittleren Unternehmen, die bislang noch keine hinreichende Beachtung gefunden haben, obwohl diese Unternehmen besonders insolvenzgefährdet sind. Die Schwierigkeit bei der Insolvenzprognose von kleinen und...
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This paper introduces a reinforcement learning based approach to compute optimal interest rate reaction functions in terms of fulfilling inflation and output gap targets. The method is generally flexible enough to incorporate restrictions like the zero lower bound, nonlinear economy structures...
Persistent link: https://www.econbiz.de/10012792732
This text was written as part of the project Modelling of Complex Systems for Public Policy. It reviews the classical authors who jointly contributed to establish the elements of what could constitute a "science of complexity". Based on the original writings of these authors, the text discusses...
Persistent link: https://www.econbiz.de/10012057058
In this paper we apply statistical inference techniques to build neural network models which are able to explain the prices of call options written on the German stock index DAX. By testing for the explanatory power of several input variables serving as network inputs, some insight into the...
Persistent link: https://www.econbiz.de/10011622006
In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification...
Persistent link: https://www.econbiz.de/10011622013
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Die Anwendung neuronaler Netzwerke ist ein extrem kontrovers diskutiertes Thema. In dem einen Extrem erhoffen die Verfechter neuronaler Netzwerke, daß diese als Produkt der 'künstlichen Intelligenz' in der Lage sind, Aufgaben zu lösen, die bislang als unlösbar oder nur schwer lösbar galten....
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