I am studying the article Barcelona baby boom: does sporting success affect birth rate?.
They are using month-data for their analysis of birth. They have normalised one month to be 30 days. So they have somehow cut values from one month to another.
There is a paragraph about removing abnormalities or probably better said gaps in the data
To evaluate a possible abnormality in a specific period, a single covariate (“intervention”) was included in the model under two different assumptions: (1) a one-time change modelled by using a covariate with zeros in all observations except a 1 in the pertinent month; and (2) a transitory change modelled by using a covariate with a value of 1 in the relevant month, which decreases exponentially over subsequent months by a factor of 0.7 (1, 0.71 , 0.72, and so on). The value of 0.7 was chosen by following standard recommendations.12 To estimate the percentage increase, we applied a logarithmic transformation to the series.
Assume that you have day data about the same event. Do you need to make such in manipulations to the data?
I may have misunderstood the purpose of the paragraph. If so, please, describe it to me.