Summary
Modelling nonlinear relationships plays an important role in recent econometric development. A promising approach to model some of the nonlinearities of a time series is to choose simple extensions of linear time series models such as bilinear models. In this contribution we investigate the bilinear time series model in a Bayesian framework. Inference is made using Markov chain methods like the Gibbs sampler and the Metropolis algorithm. Two versions of the Metropolis algorithm are compared in a numerical study.
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© 1996 Springer-Verlag Berlin · Heidelberg
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Ickstadt, K., Jin, S., Polasek, W. (1996). Metropolis Sampling in Bilinear Time Series Models. In: Bock, HH., Polasek, W. (eds) Data Analysis and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-80098-6_26
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DOI: https://doi.org/10.1007/978-3-642-80098-6_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-60774-8
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