This paper presents a rigurous framework for evaluating alternative forecasting methods for Chilean industrial production and sales. While nonlinear features appear to be important for forecasting the very short term, simple univariate linear models perform about as well for almost every...

This paper is a step towards the econometric foundation of computational intelligence in finance. Financial time series modeling and forecasting are addressed with an artificial neural network, examining issues of its topology dependency. Structural dependency of results is viewed not as a...

This paper investigates the precision of multivariate models of the output gap and considers their implications for the formulation of macroeconomic policy. Multivariate models identify the gap by including information from structural economic relationships, such as Okun's Law, the Phillips...

This paper examines evidence of long- and short-run co-movement in Canadian sectoral output data. Our framework builds on a vector-error-correction representation that allows to test for and compute full-information maximum-likelihood estimates of models with codependent cycle restrictions. We...

This paper focuses on the time series’ decomposition and economic representation of its constituent parts. Wavelet transforms are used for adaptive analysis of local behaviour of heterogeneous agents. Unlike fully revealing equilibrium of homogeneous beliefs, in the environment with...

The performance of Monte Carlo integration methods like importance-sampling or Markov-Chain Monte-Carlo procedures depends greatly on the choice of the importance- or candidate-density. Such a density must typically be "close" to the target density to yield numerically accurate results with...

An efficient procedure is proposed to evaluate option prices using neural networks. The method considers alternatives to the procedures suggested by Hutchinson, Lo and Poggio in the Journal of Finance of 1994

One of the most critical issues when using neural networks is how to select appropriate network architectures for the problem at hand. Practitioners usually refer to information criteria which might lead to over-parameterized models with heavy consequence on overfitting and poor ex-post forecast...

Here artificial neural networks (ANNs) are employed for efficiency purposes. First, the main features of ANNs are presented. Then, common techniques of the efficiency literature are reviewed: parametric (deterministic and stochastic) and non-parametric (Data Envelopment Analysis [DEA] and Free...