I am currently working on a project in which I am trying to create a better forecast using extraneous variables, in particular a dummy variable representing whether a hurricane makes landfall to the US in a given month.
For example, let's say that I am given a monthly forecast (from a software package that uses a univariate approach taking into account 36 months of data and the item's seasonality and trends). For whatever reason, on this item the impact of a hurricane in a particular month is not picked up by the software's forecast.
With that being said, I would like to figure out the impact that a hurricane has on the demand for this item, and then add that result to the forecast. To do so, I would run a regression on the actual demand and lags of a hurricane dummy (lag 3 is the last lag to be statistically significant in this example) and find the impact at each lag. Then add these effects to the software's forecast as necessary when a hurricane occurs on a given month.
My question is, would this be valid way to attack this problem? If not, what are some other options for doing so? Any help is greatly appreciated!
Side note: from a practical perspective, from past business knowledge, it is known that a hurricane doesn't have impact on current months demand. Only future months. This would mean that for the purposes of my project, I don't need to predict whether a hurricane will occur in a month or not, just what impact this hurricane would have on future months after it has occurred.
Additionally, I know that there are a lot of other factors at play here (category of hurricane, where it makes landfall, etc.), but for now I just want to use the dummy variable mentioned above.