In this paper we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model...

This paper is concerned with modelling time series by single hidden-layer feedforward neural network models. A coherent modelling strategy based on statistical inference is presented. Variable selection is carried out using existing techniques. The problem of selecting the number of hidden units...

This paper contains a forecasting exercise on 30 time series, ranging on several fields, from economy to ecology. The statistical approach to artificial neural networks modelling developed by the author is compared to linear modelling and to other three well-known neural network modelling...

We develop a structural bond pricing approach and implement it on a large panel of US industrial bonds using an efficient maximum likelihood methodology. We evaluate the model's ability to predict yield spread levels and changes out-of-sample. Errors are smaller and distinctly less variable than...

This paper analyzes how bond option prices are affected by different types of monetary policy. Analytical results from a general equilibrium model with sticky wages show that employment or output targeting typically give lower bond option prices than inflation targeting. -- inflation targeting ;...