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Decisions and performance under bounded rationality : a computational benchmarking approach

Zēgners, Dainis; Sunde, Uwe; Strittmatter, Anthony - 2020

This paper presents a novel approach to analyze human decision-making that involves comparing the behavior of …

Persistent link: https://www.econbiz.de/10012499843

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'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

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

Prognose von Insolvenzwahrscheinlichkeiten mit Hilfe logistischer neuronaler Netzwerke : eine Untersuchung von kleinen und mittleren Unternehmen

Anders, Ulrich; Szczesny, Andrea - 1996

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...

Persistent link: https://www.econbiz.de/10011619995

Prognose von Insolvenzwahrscheinlichkeiten mit Hilfe logistischer neuronaler Netzwerke : eine Untersuchung von kleinen und mittleren Unternehmen

Anders, Ulrich; Szczesny, Andrea - 1996

Persistent link: https://www.econbiz.de/10013428062

Testing for Granger causality in large mixed-frequency VARs

Götz, Thomas B.; Hecq, Alain W. J.; Smeekes, Stephan - 2015

We analyze Granger causality testing in a mixed-frequency VAR, where the difference in sampling frequencies of the variables is large. Given a realistic sample size, the number of high-frequency observations per low-frequency period leads to parameter proliferation problems in case we attempt to...

Persistent link: https://www.econbiz.de/10011415576

Putting a human in the loop : increasing uptake, but decreasing accuracy of automated decision-making

Sele, Daniela; Chugunova, Marina - 2023

discussions about regulation of automated decision-making. In an online experiment, we find that 66% of participants prefer … algorithmic to equally accurate human recommendations if the decision is delegated fully. This preference for algorithms increases … by further 7 percentage points if participants are able to monitor and adjust the recommendations before the decision is …

Persistent link: https://www.econbiz.de/10014495079

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 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

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

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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