"Spurious regression" (in the context of time series) and associated terms like unit root tests are something I've heard a lot about, but never understood.
Why/when, intuitively, does it occur? (I believe it's when your two time series are cointegrated, i.e., some linear combination of the two is stationary, but I don't see why cointegration should lead to spuriousness.) What do you do to avoid it?
I'm looking for a high-level understanding of what cointegration/unit root tests/Granger causality have to do with Spurious regression (those three are terms I remember being associated with spurious regression somehow, but I don't remember what exactly), so either a custom response or a link to references where I can learn more would be great.