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Inference for adaptive time series models : stochastic volatility and conditionally Gaussian state space form

Bos, Charles S.; Shephard, Neil G. - 2004

In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are ineffective, but that this problem can be removed by...

Persistent link: https://www.econbiz.de/10011334849

Information flows around the globe : predicting opening gaps from overnight foreign stock price patterns

Gooijer, Jan G. de; Diks, Cees G. H.; Gatarek, Lukasz T. - 2009

This paper describes a forecasting exercise of close-to-open returns on major global stock indices, based on price patterns from foreign markets that have become available overnight. As the close-to-open gap is a scalar response variable to a functional variable, it is natural to focus on...

Persistent link: https://www.econbiz.de/10011379456

New neural network methods for forecasting regional employment : an analysis of German labour markets

Patuelli, Roberto; Reggiani, Aura; Nijkamp, Peter; … - 2006

patterns in Germany. NNs are modern statistical tools based on learning algorithms that are able to process large amounts of … compares two NN methodologies. First, it uses NNs to forecast regional employment in both the former West and East Germany … differences in the size and time horizons of the data, the forecasts for West and East Germany are computed separately. The out …

Persistent link: https://www.econbiz.de/10011348710