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Predicting monetary policy using artificial neural networks
Hinterlang, Natascha - 2020
This paper analyses the forecasting performance of monetary policy reaction functions using U.S. Federal Reserve … forecasting purposes. Overall, evidence suggests that U.S. monetary policy behaviour between1987-2012 is nonlinear. …
Persistent link: https://www.econbiz.de/10012256503
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Complexity : a review of the classics
Furtado, Bernardo Alves; Sakowski, Patrícia Alessandra … - 2015
This text was written as part of the project Modelling of Complex Systems for Public Policy. It reviews the classical …
Persistent link: https://www.econbiz.de/10012057058
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Optimal monetary policy using reinforcement learning
Hinterlang, Natascha; Tänzer, Alina - 2021
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
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Using machine learning for measuring democracy : an update
Gründler, Klaus; Krieger, Tommy - 2021
We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016, European Journal of Political Economy). Four improvements are particularly notable: First, we produce a...
Persistent link: https://www.econbiz.de/10012431822
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Prediction of corporate credit ratings with machine learning : simple interpretative models
Galil, Koresh; Hauptman, Ami; Rosenboim, Rosit Levy - 2023
Persistent link: https://www.econbiz.de/10014634152
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Benchmarking short term forecasts of regional banknote lodgements and withdrawals
Sonnleitner, Benedikt; Stapf, Jelena; Wulff, Kai - 2024
retailers. Forecasting the demand for cash on a granular level is crucial in the process to keep logistics costs low, while … models to define banknote production for the coming years, our contribution is to combine features of macro level forecasting … forecasting methods on granular level can substantially improve inventory performance for this use-case. To guide the …
Persistent link: https://www.econbiz.de/10015079884
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Improving the pricing of options : a neural network approach
Anders, Ulrich; Korn, Olaf; Schmitt, Christian - 1996
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
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Model selection in neural networks
Anders, Ulrich; Korn, Olaf - 1996
, it is suggested that a statistical analysis should become an integral part of neural network modelling. …
Persistent link: https://www.econbiz.de/10011622013
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Artificial intelligence and firm-level productivity
Czarnitzki, Dirk; Fernández, Gastón P.; Rammer, Christian - 2022
Artificial Intelligence (AI) is often regarded as the next general-purpose technology with a rapid, penetrating, and far-reaching use over a broad number of industrial sectors. A main feature of new general-purpose technology is to enable new ways of production that may increase productivity. So...
Persistent link: https://www.econbiz.de/10012819685
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AI-tocracy
Beraja, Martin; Kao, Andrew; Yang, David Y.; Yuchtman, Noam - 2021
Persistent link: https://www.econbiz.de/10012655860