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Compositional Time Series: An Application

Bergman, Jakob LU orcid (2008) CODAWORK'08
Abstract
The composition of the labour force is an important economic factor for a country. Often the changes in proportions of different groups are of interest. In this paper we study a monthly compositional time series from the Swedish Labour Force Survey from 1994 to 2005. Three models are studied: the ILR-transformed series, the ILR-transformation of the compositional differenced series of order 1, and the ILRtransformation of the compositional differenced series of order 12. For each of the three models a VAR-model is fitted based on the data 1994-2003. We predict the time series 15 steps ahead and calculate 95 % prediction regions. The predictions of the three models are compared with actual values using MAD and MSE and the prediction regions... (More)
The composition of the labour force is an important economic factor for a country. Often the changes in proportions of different groups are of interest. In this paper we study a monthly compositional time series from the Swedish Labour Force Survey from 1994 to 2005. Three models are studied: the ILR-transformed series, the ILR-transformation of the compositional differenced series of order 1, and the ILRtransformation of the compositional differenced series of order 12. For each of the three models a VAR-model is fitted based on the data 1994-2003. We predict the time series 15 steps ahead and calculate 95 % prediction regions. The predictions of the three models are compared with actual values using MAD and MSE and the prediction regions are compared graphically in a ternary time series plot. We conclude that the first, and simplest, model possesses the best predictive power of the three models. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
ILR, VAR models, ternary time series plot, Compositional time series
host publication
Proceedings of CODAWORK'08, The 3rd Compositional Data Analysis Workshop, CD-ROM
editor
Daunis-i-Estadella, J. and Martín-Fernández, J.A.
publisher
Univeristy of Girona, Girona (Spain)
conference name
CODAWORK'08
conference location
Girona, Spain
conference dates
2008-05-27 - 2008-05-30
ISBN
978-84-8458-272-4
language
English
LU publication?
yes
id
9aff2633-8aa1-433b-8575-af1035bb91f9 (old id 1388013)
date added to LUP
2016-04-04 11:50:00
date last changed
2018-11-21 21:07:30
@inproceedings{9aff2633-8aa1-433b-8575-af1035bb91f9,
  abstract     = {{The composition of the labour force is an important economic factor for a country. Often the changes in proportions of different groups are of interest. In this paper we study a monthly compositional time series from the Swedish Labour Force Survey from 1994 to 2005. Three models are studied: the ILR-transformed series, the ILR-transformation of the compositional differenced series of order 1, and the ILRtransformation of the compositional differenced series of order 12. For each of the three models a VAR-model is fitted based on the data 1994-2003. We predict the time series 15 steps ahead and calculate 95 % prediction regions. The predictions of the three models are compared with actual values using MAD and MSE and the prediction regions are compared graphically in a ternary time series plot. We conclude that the first, and simplest, model possesses the best predictive power of the three models.}},
  author       = {{Bergman, Jakob}},
  booktitle    = {{Proceedings of CODAWORK'08, The 3rd Compositional Data Analysis Workshop, CD-ROM}},
  editor       = {{Daunis-i-Estadella, J. and Martín-Fernández, J.A.}},
  isbn         = {{978-84-8458-272-4}},
  keywords     = {{ILR; VAR models; ternary time series plot; Compositional time series}},
  language     = {{eng}},
  publisher    = {{Univeristy of Girona, Girona (Spain)}},
  title        = {{Compositional Time Series: An Application}},
  url          = {{https://lup.lub.lu.se/search/files/5865646/1388886.pdf}},
  year         = {{2008}},
}