In this case study on treasury yeilds from MIT, I have a question on page 11-12. He uses this data
getYahooData("^TNX", start=20000101, end=20130531)
His logic on page 11 is this
- "only daily is close to being stationary" (I get this)
- "we observe lower p value when higher freq frequency" (I see this since p-value daily < p-value monthly)
- "so stronger time series structure at higher freq, hence we diffrence the data"
for that data, we reject H0: non-stationary.
I want to understand how he can go from seeing the p-values are lower for daily (closer to stationary) into knowing that differenced data will reject H0.