What is wrong with lagged regressor in time series regression? I am trying to explain a time series with the help of other related series. I really get nice fits using a standard LM approach with NeweyWest VC matrix. The fit even increases drastically when I replace some of the explanatories by higher lags (lag 10) of these explanatories (quarterly series) to the mix.  
Is it ok, to use such high lags? The length of the series is about 120. What's the risk of using high lags? At least it seems uncommon to me to use it. 
 A: It's not "wrong," but it's probably a bad idea. One problem is that you don't have lags for your first ten observations, so you can't use those in your analysis, effectively making your data set smaller.
There are certain lags that we think make sense intuitively: Last period probably effects this period, this time this year is probably related to this time next year due to some seasonal variation patterns. One lag and four lags for you would make sense. Having two years out influence what happens today would be surprising and 2.5 years (or 10 quarters) seems stranger still. 
I would chalk up a significant lag at quarter 10 to chance, rather than a good model. Including this lag can lead to overfitting. If you overfit, you will have trouble with out-of-sample forecasting. As a test on this, you might run the model with the 10 quarter lag on the first and second halves of your data to see if you still get a significant/similar result.
Lastly, except in the cases of intuition, I don't like to include one lag, then skip a bunch, then include another. For example, including lags 1 and 4 makes sense intuitively, so that's fine, but adding lags 1, 4, and 10 just seems strange.
Time series is as much art as science, so it does take some playing around.
A: What you are doing with using lags of explanatories is in my opinion a very bad idea if the explanatory variable is auto-correlated with itself. The lags are correlated thus identification will probably fail as you are trying to use ESTIMATION to perform IDENTIFICATION. Box and Jenkins and a host of others suggest pre-whitening to identify the correct X structure in an ARMAX model. Art becomes science when you understand what to do. Time series is not art but intelligent analytics done artfully make the science a lot easier. In my opinion the art "nearly vanishes" as you hone the science.   
