The more the merrier when it comes to the number of observations we have for our economic time-series data - right? Well, not necessarily.
There are several reasons to be cautious, not the least of which include the possibility of structural breaks or regime-switching in the data-generating process. However, these are topics for future posts. Here, I want to discuss a different issue - namely, the impact of data frequency on the properties of tests for the stationarity of economic time-series.
To be specific, let's consider the following question: "Which is better when I'm applying the (augmented) Dickey-Fuller test - 20 annual observations for the series, or 80 quarterly observations?"
To be specific, let's consider the following question: "Which is better when I'm applying the (augmented) Dickey-Fuller test - 20 annual observations for the series, or 80 quarterly observations?"