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Then we gave an overview of existing economic applications of neural networks, where we distinguished between three types: Classification of economic agents, time series prediction and the modelling of bounded rational agents.
Neural Networks – originally inspired from Neuroscience – provide powerful models for statistical data analysis. Their most prominent feature is their ability to “learn” dependencies based on a finite number of observations.
Neural Networks - originally inspired from Neuroscience - provide powerful models for statistical data analysis. Their most prominent feature is their ability to "learn" dependencies based on a finite number of observations.
The chapters of this dissertation explore the theoretical and empirical potential of neural networks and deep learning as estimation techniques in economics. The rst chapter pro-vides a novel approximation result for two hidden layer neural networks that makes clear the trade-o between width and depth.
1 Ιαν 1999 · we present an application of neural networks to financial markets, experimenting with various learning mechanisms that may describe reasonable behavioral rules followed by agents acting under...
23 Μαρ 2021 · Our study uses the grey relational analysis (GRA) and artificial neural network (ANN) models for the prediction of consumer exchange-traded funds (ETFs). We apply eight variables, including the put/call ratio, the EUR/USD exchange rate, the volatility index, the Commodity Research Bureau Index (CRB), the short-term trading index, the New York ...
31 Αυγ 1998 · Neural Networks – originally inspired from Neuroscience – provide powerful models for statistical data analysis. Their most prominent feature is their ability to “learn” dependencies based ...
