La financiación de la innovación se ha caracterizado por ser un campo fragmentado donde coexisten múltiples teorías, cada una enfocada en uno o en unos pocos aspectos de la financiación a la vez. El presente artículo busca identificar las variables financieras que más influyen y su efecto...

In this study the ability of artificial neural network (ANN) in forecasting the daily NASDAQ stock exchange rate was investigated. Several feed forward ANNs that were trained by the back propagation algorithm have been assessed. The methodology used in this study considered the short-term...

Jährlich veröffentlicht das Statistische Bundesamt Statistiken über die Besteuerung von Rentnerinnen und Rentnern, wegen langer Abgabe- und Einspruchsfristen für Einkommensteuererklärungen allerdings erst etwa 3,5 Jahre nach Ablauf des betref- fenden Statistikjahres. Jedoch liegt ein Teil...

Understanding how energy use evolves at different stages of development is essential for reliable prospective analysis and planning. With that aim in mind, this paper examines the composition of residential energy consumption and its sensitivity to income changes, distinguishing fuel types and...

We investigate economic and institutional determinants of ICT infrastructure for a broad cross section ofmore than 100 countries. The ICT variable is constructed from a principal components analysis. The explanatory variables are selected by variants of the Lasso estimator from the machine...

We introduce automation into a standard model of capital accumulation and show that (i) there is the possibility of perpetual growth, even in the absence of technological progress; (ii) the long-run economic growth rate declines with population growth, which is consistent with the available...

Can neural networks learn to select an alternative based on a systematic aggregation of conflicting individual preferences (i.e. a 'voting rule')? And if so, which voting rule best describes their behavior? We show that a prominent neural network can be trained to respect two fundamental...

Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the...

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in...