Abstract
The paper presents an approach to modelling and forecasting the Industrial Production Index which allows to account for the presence of asymmetric effects in both the conditional mean and the conditional variance. More precisely, the proposed approach combines a Self Exciting Threshold AutoRegressive (SE- TAR) model for the conditional mean with a conditional heteroskedastic model fitted to the residuals. Namely, we use a Constrained Changing Parameters Volatility (CPV-C) model which allows to capture asymmetries in the conditional variance dynamics by means of interaction terms between past shocks and volatilities. The out of sample fitting performance of the model is evaluated by means of an application to a time series of U.S. data.
This paper was supported by the MURST COFIN 2000 project : ‘Modelli Sto castici e Metodi di Simulazione per l’Analisi di Dati Dip endenti’ .
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Amendola, A., Storti, G. (2004). Non-linear Dynamics in the Industrial Production Index. In: Bock, HH., Chiodi, M., Mineo, A. (eds) Advances in Multivariate Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17111-6_12
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DOI: https://doi.org/10.1007/978-3-642-17111-6_12
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