I have a revenue dataset for various businesses. For about half of those businesses, monthly data is available. For the other half, only annual revenue data is present. I know the seasonality of the revenues fairly well from the half of the businesses who have reported their monthly revenue. In addition, the data goes back at least five years, so I know that seasonal effects are real. What is the best way to go about extrapolating monthly revenue for businesses with missing data given the information I have on hand? Even though it is not strictly true, I am assuming that the businesses with missing data and businesses with complete data are sufficiently similar that we need not worry about any underlying issues.
Time series analysis incorporating Intervention Detection can estimate these missing values. Essentially the "0" value at a particular point in time is treated as an exception and the size of the exception is the estimated coefficient associated with that point in time. You can search for Intervention Detection OR AUTOMATIC Intervention Detection using a browser. If the number of missing values is "large" relative to normal data points then you might have to iteratively pursue this.