What are some examples of "real world" problems that can be modeled using vector autoregressive models? I would like to stay away from finance and economic examples since they are too abstract for me to understand.  Are there any "real world" examples for example healthcare, exam marks, environmental science and so on?  From a managerial perspective I would like a basic understanding of how VAR could potentially be used to model real world phenomenon.
 A: Do not get hung up by the Finance or Economics tags.  
VAR is a time-series technique.  This bears repeating: time-series, and nothing but.  Its key innovation, back in the day, was that it removed all underlying domain-specific information encoded in a model and reduced it to pure time series interaction:  in the simplest case, does the past of series A and B affect either one or both of A and B?  If one series is shocked, how long does a change persist etc.  Economics, particularly macroeconomics, simply became a big application area as there are often time series that are interrelated, and a model-free way (in the VAR sense) can not only be useful for model fitting but also for forecasts from these models.
A: I'm using VAR to model retail employment in census blocks near transit stations. My vector is the time series of the 300+ census blocks within 1/2 mile of a transit station. I am attempting to find out if the total retail employment is significantly different after the advent of transit than before, and if the total employment is significantly different than it would have been without the addition of transit. So I'm using VAR to model my time series, using my VAR model to predict new data, and then comparing that new data to actual data during the years after the transit station opened.
