Lagging/Leading Indicator Length Time I tried looking this question up on google and didn't find material that answered my question. But my questions are:
(1)  Is there a method to determine how long it takes a leading indicator to affect a variable ? So if we are looking at the affects of oil production on sales, when oil drops how long does it take to affect sales.
Could I use survival analysis for this? This seems related but in a biological context 
(2) Can we measure the degree to which oil production affects sales? If oil production drops by 10% it affects sales by 17%.
(3) What's the best way to determine the most important leading indicator? Univariate regression and compare models?
(4) Is there a package in R that could be used for this?
 A: *

*Go to Autobox.com and review the discussion about transfer functions.

*The is a question about elasticities. Review any principles of economics text. A new version of Autobox calculates elasticities.

*If your data is time series, it is inappropriate to use classical regression analysis. Applying classical regression analysis to summarize time series data will result in a violation of one or more of the underlying assumptions about the error terms. Use time series techniques to summarize time series data, and classical regression to summarize cross sectional data. 

*If you focus on R, then you will spend an inordinate amount to time learning how to program rather than on analysis. Investigate the advantages of Autobox.

A: (1)To find lead/lag indicator time Cross Correlation Function can be used in R 
with the ccf command.
(2)To find the effect of the indicator  $x_{(x+h)}$ on $y_{x}$ you can regress on the lag function and use the coefficient $\beta_{1}$ in the regression as the magnitude. As found here
