I have a time series dataset. The dataset has a number of variables (Date, revenue, variable1, variable2, variable3). All data is continuous and numeric. The data has over 1000 observations, with each observation being one day as the Dategoing from 2016-2020. I want to produce a regression model such that
The 3 independent variables above are such that their effects on revenue cannot be seen immediantly, but can take up to 7 days. For this reason, I am producing a moving sum over 7 days for all 4 variables, to produce 4 new transformed variables. I was then going to use these transformed variables in the above mentioned regression model. To me this made more sense, as using a single day as a data point would not capture the effects fully of each of the 3 independent variables.
My question is, is this a valid way to go about undertaking linear regression? Am I introducing bias into the results? My purpose is to use the model to explain the effects of changing the independent variable on the revenue.