The flexibility of neural networks to handle complex data patterns of economic variables is well known. In this survey we present a brief introduction to a neural network and focus on two aspects of its flexibility . First, a neural network is used to recover the dynamic properties of a...

In this paper neural networks are fitted to the real exchange rates of seven industrialized countries. The size and topology of the used networks is found by reducing the size of the network through the use of multiple correlation coefficients, principal component analysis of residuals and...

The performance of Monte Carlo integration methods like importance sampling or Markov Chain Monte Carlo procedures greatly depends on the choice of the importance or candidate density. Usually, such a density has to be "close" to the target density in order to yield numerically accurate results...

In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from...

In Hoogerheide, Kaashoek and Van Dijk (2002) the class of neural network sampling methods is introduced to sample from a target (posterior) distribution that may be multi-modal or skew, or exhibit strong correlation among the parameters. In these methods the neural network is used as an...

The flexibility of neural networks to handle complex data patterns of economic variables is well known. In this survey we present a brief introduction to a neural network and focus on two aspects of its flexibility . First, a neural network is used to recover the dynamic properties of a...

A major problem in applying neural networks is specifying the size of the network. Even for moderately sized networks the number of parameters may become large compared to the number of data. In this paper network performance is examined while reducing the size of the network through the use of...

In this article we describe reinforcement learning, a machine learning technique for solving sequential decision problems. We describe how reinforcement learning can be combined with function approximation to get approximate solutions for problems with very large state spaces. One such problem...

Using annual data on real Gross Domestic Product per capita of seventeen industrialized nations in the twentieth century the empirical relevance of shocks, trends and cycles is investigated. A class of neural network models is specified as an extension of the class of vector autoregressive...

Likelihoods and posteriors of instrumental variable regression models with strong endogeneity and/or weak instruments may exhibit rather non-elliptical contours in the parameter space. This may seriously affect inference based on Bayesian credible sets. When approximating such contours using...