What to do with large number of collinear variables?
First, if you're doing regression on a time series, you have to check for autocorrelation or your p-values and significance tests are going to be highly inaccurate. Also, the R^2 value you will get will be seriously misleading if you don't account for autocorrelation.

Secondly, if you are using lagged variables, it's highly likely that many of them are going to be correlated.

Collinearity affects linear regression models by making it difficult for the model to determine which coefficient is causing the effect on the dependent variable. The result is that between highly collinear variables, you will have inaccurate p-values and very small coefficients or even coefficients with the wrong signs.

Multicollinearity can be ignored when you are using some of those variables as control variables or when you don't care very much about interpretation; furthermore, collinearity doesn't matter when the variables of interest are not collinear.

Cross-validated has many good discussions on regression as well.