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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

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

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

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

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

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

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

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

Emotion in euro area monetary policy communication and bond yields : the Draghi era

Kanelis, Dimitrios; Siklos, Pierre L. - 2025

We combine modern methods from Speech Emotion Recognition and Natural Language Processing with high-frequency financial data to precisely analyze how the vocal emo- tions and language of ECB President Mario Draghi affect the yields and yield spreads of major euro area economies. This novel...

Persistent link: https://www.econbiz.de/10015419938

The WebAI paradigm of innovation research : extracting insight from organizational web data through AI

Dahlke, Johannes; Schmidt, Sebastian; Lenz, David; … - 2025

This paper introduces the WebAI paradigm as a promising approach for innovation studies, business analytics, and informed policymaking. By leveraging artificial intelligence to systematically analyze organizational web data, WebAI techniques can extract insights into organizational behavior,...

Persistent link: https://www.econbiz.de/10015415304