how to choose predictive variables in my time series regression model

Business knowldege (domain exeprtise) could defintely help in pruning the set of variables from the starting set of 300 to a smaller set. But even if you cut it down to a 100 variables, taking those and lags of different orders on these variables, you could have an overwhelming number of "explanatory" variables to forecast the dependent variable (daily sales). Sometimes a model in which the lag of the dependent variable is used as an explanatory variable along with the other selected variables among the 300 (perhaps with lags of a few of a them, based on intuition) will not only reduce the number of explantory variables and thereby increase the degrees of freedom for the prediction model but also provide more stable predictions. Also one can make use of the first so many principal components among the chosen predictor variables to deal with multicollinearity issues which typically arise in such probelms. This also cuts down the number of parameters and thereby increases the df of the model predictions.