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Markov-switching generalized additive models

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Glennie_2017_SC_Markov_switching_CC.pdf (927.7Kb)
Date
01/01/2017
Author
Langrock, Roland
Kneib, Thomas
Glennie, Richard
Michelot, Théo
Keywords
P-splines
Hidden Markov model
Penalized likelihood
Times series regression
QA Mathematics
QA75 Electronic computers. Computer science
3rd-DAS
BDC
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Abstract
We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regime switching controlled by an unobservable Markov chain. Building on the powerful hidden Markov model machinery and the methods for penalized B-splines routinely used in regression analyses, we develop a framework for nonparametrically estimating the functional form of the effect of the covariates in such a regression model, assuming an additive structure of the predictor. The resulting class of Markov-switching generalized additive models is immensely flexible, and contains as special cases the common parametric Markov-switching regression models and also generalized additive and generalized linear models. The feasibility of the suggested maximum penalized likelihood approach is demonstrated by simulation. We further illustrate the approach using two real data applications, modelling (i) how sales data depend on advertising spending and (ii) how energy price in Spain depends on the Euro/Dollar exchange rate.
Citation
Langrock , R , Kneib , T , Glennie , R & Michelot , T 2017 , ' Markov-switching generalized additive models ' , Statistics and Computing , vol. 27 , no. 1 , 1406.3774 , pp. 259-270 . https://doi.org/10.1007/s11222-015-9620-3
Publication
Statistics and Computing
Status
Peer reviewed
DOI
https://doi.org/10.1007/s11222-015-9620-3
ISSN
0960-3174
Type
Journal article
Rights
© The Author(s) 2015. This article is published with open access at Springerlink.com. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/10578

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