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Common factors in conditional distributions

Granger, C. W. J. (contributor); … - 2002 - [Elektronische Ressource]

The concept of common factors has in the econometrics literature been applied to conditional means or in some cases to conditional variances. In this paper we generalize this concept to bivariate distributions. This is done using the conditional bivariate copula as the statistical tool. The...

Persistent link: https://www.econbiz.de/10001714617

A nonlinear time series model of El Niño

Hall, Anthony D.; Skalin, Joakim; Teräsvirta, Timo - 1998

Persistent link: https://www.econbiz.de/10000994162

On the effects of imposing or ignoring long memory when forecasting

Andersson, Michael K. - 1998

Persistent link: https://www.econbiz.de/10000981126

Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series : a re-examination

Teräsvirta, Timo (contributor); Dijk, Dick van (contributor) - 2004 - [Elektronische Ressource], Rev. November 9, 2004

In this paper we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and … class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also … importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained …

Persistent link: https://www.econbiz.de/10002127012

On seasonal error correction when the processes include different numbers of unit roots

Lyhagen, Johan (contributor); Löf, Mårten (contributor) - 2001 - [Elektronische Ressource], Rev. March 15, 2001

forecast mean squared error criterion and certain parameter estimation results indicate that, in practice, a cointegration … forecast, is an ordinary VAR model, also in annual differences. -- Seasonal cointegration ; forecasting …

Persistent link: https://www.econbiz.de/10001600047

Forecasting with artificial network models

Rech, Gianluigi (contributor) - 2002 - [Elektronische Ressource]

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...

Persistent link: https://www.econbiz.de/10001645582