Let me try and explain the rolling window regression that I have used in my analysis here. Rolling window regression for a timeseries data is basically running multiple regression with different overlapping (or non-overlapping) window of values at a time. For example, if your dataset has values on a timeseries with 100 observations and you want to perform rolling regression, or for that matter any operation on a rolling window, the idea is to start with an initial window of say 40 values(1st to the 40th observation) perform the operation that you wish to and then roll the window with some values, lets say we roll the window by 5. Now, the second window of data would be the next 40 observations starting from the 5th observation (5th to the 45th observation). Similarly, the third window will be the next 40 values starting from the 10th value, and so on. The advantage of using this technique is basically to look at any changing property of a series over time. You will get an estimate of the property over time instead of one single constant measure for the entire period.
Gostou? Eu também. Mas vou deixar para ver isto depois.