Why Is Cointegration Important In Practice? In one of my econometrics classes. I'm nearly finished a problem set and assigned readings on an introduction to cointegration. I've essentially finished the assignment, run dickey fuller tests on some data, estimated cointegration relationships, and estimated an error correction model. But I'm often surprised at how textbooks in econometrics tend to drive right away into the details without first explaining the big picture and how various concepts are applied in practice. I was wondering:

Could anyone offer an intuitive explanation on how cointegration is important in practice. For example, if you are building models for policy analysis, or finance, or macroeconomic analysis, could you talk about some practical instances where cointegration comes into play and why it is important in these instances?

Thanks very much everyone for the help!
I think it is a very classical economic teaching problem - showing how something is relevant in the real world. 

First, it solved a problem where linear regressions could lead to spurious results: https://en.wikipedia.org/wiki/Cointegration


  ...Before the 1980s many economists used linear regressions on
  (de-trended[citation needed]) non-stationary time series data, which
  Nobel laureate Clive Granger and Paul Newbold showed to be a dangerous
  approach that could produce spurious correlation, since standard
  detrending techniques can result in data that are still
  non-stationary. Granger's 1987 paper with Robert Engle formalized the
  cointegrating vector approach, and coined the term.
  
  For integrated I(1) processes, Granger and Newbold showed that
  de-trending does not work to eliminate the problem of spurious
  correlation, and that the superior alternative is to check for
  co-integration. Two series with I(1) trends can be co-integrated only
  if there is a genuine relationship between the two. Thus the standard
  current methodology for time series regressions is to check all-time
  series involved for integration. If there are I(1) series on both
  sides of the regression relationship, then it's possible for
  regressions to give misleading results...


Second, our "Sister site" actually does a nice job explaning the intuition behind, which can explain why it is important in the real world - are two different time series related? And can we test for this?: 

https://quant.stackexchange.com/questions/219/what-is-the-intuition-behind-cointegration 

I really like this one: 


  "Think of a man walking his dog. He will go along and his dog will
  stroll along running back and forth. Man and dog are mathematically
  "cointegrated".
  
  As an investor you bet that the dog is coming back to his master or
  that the leash has only a certain length.