I have the value of a dependent variable, Y, for every day from 1/1/2000 to 12/31/2016. The value is partially dependent on the day of the year/week/month, the month, and the year; as well as on other variables. I'm training my model using 2000-2015 and testing using 2016. If I have a model that predicts Y based on the day and factors X...X[N], how do I determine whether any added accuracy from new factor X[N+1] is statistically significant. If it weren't for the date dependency, I would use bootstrap or other random sampling techniques across multiple runs. It doesn't seem like removing random dates or days of the year is a good approach.