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Granger Causality

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Introducing Granger Causality

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• un... you are right. You can't robustly conduct Granger Causality using regular nominal time series variables that are by definition excessively autocorrelated to analyze as is. But, it is easy to convert them to % change variable. Then, Granger Causality works perfectly well.

I would have answered you right away, but the web interface of Slideshare got all screwed up and I could not post comment to answer your question.

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• Hello again Guy, just a quick question, the transformation of time series from a level variable to a % change is a pre-requisite for Grangner Casuality test as well?

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• Thanks man

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• un..., you have to convert both variables to % change. So, you explore the correlation between GDP growth in % vs enrollment change in %. Both variables should be on the same frequency (quarterly change or annual change or whatever). That's how you are going to derive the most relevant correlation or regression coefficients.

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• Guy, thanks for your feedback. Lets say i have 2 variables, GDP Growth rate X and Enrollment Numbers Y. So in my case, I think I have to transform just the enrollment numbers in %change. Is that correct?
btw, really appreciate your help on this. You are the greatest.

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Granger Causality

1. 1. Granger Causality Guy Lion December 2005
2. 2. Granger Causality vs “Causality” <ul><li>Granger causality measures whether A happens before B and helps predict B. </li></ul><ul><li>A Granger causing B may entail “real”causality. But, you can’t be sure. </li></ul><ul><li>If A does not Granger cause B. You can be more confident, A does not cause B. </li></ul>
3. 3. Granger Causality steps <ul><li>Develop a Base case autoregressive model using dependent variable and its lagged values as independent variable. </li></ul><ul><li>Develop a Test case model by adding a second lagged independent variable you want to test. </li></ul><ul><li>Calculate the square of the residual errors for the two models and run an F test or t Test (unpaired) to check if the residuals are significantly lower when you add tested second variable. </li></ul><ul><li>Redo steps 1 through 3, but reverse the direction. By comparing the tests significance or P value, you can see if A Granger causes B more than B Granger causes A. </li></ul>
4. 4. The Basic Picture Independent Variable being tested
5. 5. The Whole Picture
6. 6. Does Loan  Granger cause Deposit  Data source: quarterly basis since 2 nd quarter of 1987. Total loans and total deposits aggregated from Fed Data Flow of Funds Accounts (L109, L215, L216, L217, L222). Let’s test if Loan  Granger causes Deposit  . We will use Loan  with a 4 quarter lag since it has the highest correlation with Deposit  .
7. 7. t Test (unpaired) (Loans cause Deposits) The Test model achieved a higher adjusted R Square of 0.41 vs the Base case model’s 0.28.
8. 8. t Test (unpaired) (Deposits cause Loans) The Test model achieved a lower adjusted R Square of 0.45 vs the Base case model’s 0.46.
9. 9. Loans  Granger causes Deposits  more than the reverse