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Model-based clustering of non-Gaussian panel data
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Juárez, Miguel A. and Steel, Mark F. J. (2006) Model-based clustering of non-Gaussian panel data. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. Working papers, Vol.2006 (No.14).
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Official URL: http://www2.warwick.ac.uk/fac/sci/statistics/crism...
Abstract
In this paper we propose a model-based method to cluster units within a panel. The underlying
model is autoregressive and non-Gaussian, allowing for both skewness and fat tails, and the units
are clustered according to their dynamic behaviour and equilibrium level. Inference is addressed
from a Bayesian perspective and model comparison is conducted using the formal tool of Bayes
factors. Particular attention is paid to prior elicitation and posterior propriety. We suggest priors
that require little subjective input from the user and possess hierarchical structures that enhance
the robustness of the inference. Two examples illustrate the methodology: one analyses economic
growth of OECD countries and the second one investigates employment growth of Spanish
manufacturing firms.
Item Type: | Working or Discussion Paper (Working Paper) | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics Faculty of Science, Engineering and Medicine > Research Centres > Warwick Systems Biology Centre |
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Library of Congress Subject Headings (LCSH): | Cluster analysis, Autoregression (Statistics), Panel analysis | ||||
Series Name: | Working papers | ||||
Publisher: | University of Warwick. Centre for Research in Statistical Methodology | ||||
Place of Publication: | Coventry | ||||
Official Date: | 2006 | ||||
Dates: |
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Volume: | Vol.2006 | ||||
Number: | No.14 | ||||
Number of Pages: | 26 | ||||
Status: | Not Peer Reviewed | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 1 August 2016 | ||||
Date of first compliant Open Access: | 1 August 2016 |
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