I am trying to build a forecasting model for three products. Because I have only 20 -25 daily readings per product sales and these series have gaps during the time span of analysis, I switched to the cumulated yearly sales series.
By using them, I am trying to forecast next year's sales figure. In that sense, my whole estimation process changes to cross-sectional exercise.
However, I have some macro variables such as "inflation", "consumer confidence" etc... for three years. Because my all variables are in a yearly aggregated state, and I have limited data to convert my project to a panel time series model, I only have zero variance economic variables. Such as a variable for 2015's inflation, a variable for 2016's inflation or 2017's inflation. Do you have any suggestion to add these economic variables to my data's current structure?