A model selection tool in multi-pollutant time series: the Granger-causality diagnosis
✍ Scribed by A. Pitard; J. F. Viel
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 225 KB
- Volume
- 10
- Category
- Article
- ISSN
- 1180-4009
No coin nor oath required. For personal study only.
✦ Synopsis
When studying the eects of atmospheric pollutants on health, multicollinearity often impedes the simultaneous study of various pollutants levels or entails a large imprecision in the estimation of their parameters. Hence there is a need for methods to reduce the dimensionability of the set explanatory variables. Causality analysis provides a way to explore relationships between explanatory variables in a preliminary analysis and then to select an adequate explanatory variables subset. An approach initially developed in econometrics and based on causality Granger's de®nition is used. Granger's causality tools which deal with stationary series links, include the vector autoregressive (VAR) process. Their applicability to the epidemiologic ®eld is explored and an extension of the method providing a selection model rule is proposed.
As an example, we used this method in a ®rst step to describe the relationships between daily pollutants (NO, NO 2 , O 3 ), and in a second step to assess the proper eects of these pollutants on children's lengths of hospital stay. The results of this study add some evidence to the signi®cant eect of nitrogen oxide on length of hospital stay.