Granger Causality in R for finding the causality Inference between COVID-19 and NSEI Index.
What is Granger causality?
Granger causality is a way to investigate causality between two variables in a time series. The method is a probabilistic account of causality; it uses empirical data sets to find patterns of correlation.
Causality is closely related to the idea of cause-and-effect, although it isn’t exactly the same. A variable X is causal to variable Y if X is the cause of Y or Y is the cause of X. However, with Granger causality, you aren’t testing a true cause-and-effect relationship; What you want to know is if a particular variable comes before another in the time series. In other words, if you find Granger causality in your data there isn’t a causal link in the true sense of the word (for example, sales of Easter baskets Granger-cause Easter!). Note: When econometricians say “cause,” what they mean is “Granger-cause,” although a more appropriate word might be “precedence”