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Memory Matters: A Case for Granger Causality in Climate Variability Studies
- Source :
- Journal of Climate. 31:3289-3300
- Publication Year :
- 2018
- Publisher :
- American Meteorological Society, 2018.
-
Abstract
- In climate variability studies, lagged linear regression is frequently used to infer causality. While lagged linear regression analysis can often provide valuable information about causal relationships, lagged regression is also susceptible to overreporting significant relationships when one or more of the variables has substantial memory (autocorrelation). Granger causality analysis takes into account the memory of the data and is therefore not susceptible to this issue. A simple Monte Carlo example highlights the advantages of Granger causality, compared to traditional lagged linear regression analysis in situations with one or more highly autocorrelated variables. Differences between the two approaches are further explored in two illustrative examples applicable to large-scale climate variability studies. Given that Granger causality is straightforward to calculate, Granger causality analysis may be preferable to traditional lagged regression analysis when one or more datasets has large memory.
- Subjects :
- Atmospheric Science
010504 meteorology & atmospheric sciences
Granger causality analysis
0208 environmental biotechnology
Autocorrelation
Regression analysis
02 engineering and technology
01 natural sciences
Causality
Regression
020801 environmental engineering
Granger causality
Climatology
Linear regression
Econometrics
0105 earth and related environmental sciences
Mathematics
Subjects
Details
- ISSN :
- 15200442 and 08948755
- Volume :
- 31
- Database :
- OpenAIRE
- Journal :
- Journal of Climate
- Accession number :
- edsair.doi...........b85bfa5494357aeb153d2375c0f277da