Here are my questions:
- is there a difference between "VAR(1)" and "AR(1)"?
- Granger Causality inspects the direction of causality. In return, we receive a p-value on how much a time series is likely to contribute to a better prediction of the other.
- Choi and Varian (2009) apply an "AR" model to look whether adding Google Search to the regression improves the prediction. Here, we can measure the magnitude of the effect.
- Is it okay to apply Choi & Varian's AR method even though data is not mixed, but same frequency?
- Why seems to be there no paper that applies both Granger and Choi/Varian's method? From my naive understanding, Granger can measure the direction of the effect while Choi & Varian's model can help to measure the magnitude of the effect. As such, both analysis might make sense in a paper?