Let us say that we have an event - variable (1/ 0) that denotes the occurence of an event on a daily basis e.g. a strike. Let us now say that we have a continuous variable (sales) that that we want to forecast. The interval on which the variable is measured is monthly. We want to incorporate the event variable in a model that will be used for forecasting sales but the event variable is measured in a more detailed interval. If there were 5 days in a month that a strike took place can we say that the event variable value for that month is 5? More generally can we create a derived event variable by aggregating the values of the original dalily event variabe? In this way we will have an independent variable with value ranging from 0 (no strikes occured the specific month) to 30 (if a strike occured every day in the specific month)?
Does this process stand from a statistical point of view and is this variable going to have any predictive power?
Thnaks in advance,
Thnaks for your answer. Let us make things less complicated assuming that the dependent variable is electricity consumption and the event is a day with extremely low average temperature (day with extreme cold). With this case we don;t care about weekends and the rest of the problems that are coused in the case of strikes. Let us say that we have electricity consumption per month period so 12 observations per year. Let us also say that we have recorder the days woth extereme cold daily by using a binary variable (so approximately 365 observations per year. What would you do if you wnated to incorporate the daily event in the model of forecasting the monthly series. Would you add up the 1, would you take the percentage of days over the month that exhibit the extreme cold or what else? What do you htink is the better solution?