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    Can someone explain me the concept of co-integration analysis?
    I have data of exports and imports on yearly basis for a country. The prof says we cant run direct regression to find the relation and dependencies as the data is co-integrated.
    Need help on this pls



    never heard of data that is co-integrated.  multicollinearity is a condition where the X’s are correlated. Is that what the prof was referring to?
    I would get my moneys worth with the professor and have him or her explain this concept.



    I can’t believe everyone doesn’t know this. From my friends in Tampa, Fl at Wikipedia -Cointegration is an econometric property of time series variables. If
    two or more series are themselves non-stationary, but a linear
    combination of them is stationary, then the series are said to be
    cointegrated. For instance, a stock market index and the price of
    its associated futures contract move through time, each roughly
    following a random walk. Testing the hypothesis that there is a
    statistically significant connection between the futures price and
    the spot price could now be done by testing for a cointegrating
    vector. (If such a vector has a low order of integration it can signify
    an equilibrium relationship between the original series, which are
    said to be cointegrated of an order below one.)
    Before the 1980s many economists used linear regressions on (de-
    trended) non-stationary time series data, which Clive Granger [1]
    and others showed to be a dangerous approach, that could
    produce spurious correlation. His 1987 paper with Robert Engle [2],
    formalized the cointegrating vector approach, and coined the term.
    For his contribution to the technique’s development Clive Granger
    shared the 2003 Nobel Memorial Prize.
    It is often said that cointegration is a means for correctly testing
    hypotheses concerning the relationship between two variables
    having unit roots (i.e. integrated of at least order one).
    What does this mean? A series is said to be “integrated of order d”
    if one can obtain a stationary series by “differencing” the series d
    times. For example, suppose a stock price is 5 on Monday, 6 on
    Tuesday, 7 on Wednesday, and 8 again on Thursday. One
    differences that series by turning it into a series of daily price
    increments. In this case, if we difference just once we get 1 … 1
    …1. (This series is actually trend stationary, so should be de-
    trended rather than differenced).
    The usual procedure for testing hypotheses concerning the
    relationship between non-stationary variables was to run Ordinary
    Least Squares (OLS) regressions on data which had initially been
    differenced. Although this method is correct in large samples,
    cointegration provides more powerful tools when the data sets are
    of limited length, as most economic time-series are.



    I thought of doing a google search, but figured it would be better for the professor to explain things like this to the student.
    Was the student asking for help or quizzing the six sigma community? :-)



    I believe the student is too lazy or not smart enough to do a Google



    Thanks Stan,
    I have gone through several articles on co-integration, but am unable to put the same to use on an actual data. You may call me lazy or even a novice, as am not a statistics student but want to figure out a correlation between two factors which are possibly cointegrated.
    Let me know where can i send the data and possibly seek help from experts like you. Thanks so much for the reply

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