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Does vector autoregression (VAR) model require data to be of normal distribution? What are the pitfalls if the residuals are not of normal distribution?

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The question is too general(vague). Please, try to focus on the aspects you are interested on. – user10525 Apr 13 '12 at 10:35
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VAR models are regressions. Hence the usual regression assumptions apply. On the other hand, since VAR model admits MA($\infty$) representation, if errors are normal then the data should be normal too. The coefficients are usually estimated using OLS, hence their estimates are robust to non-normal disturbances. However if you have cointegration, the usual tests are sensitive to normality, if I remember correctly. – mpiktas Apr 13 '12 at 11:48

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Thank you. From what I understood for linear regression Yi = b0 + b1*Xi + ei,

1) b1 ~ N( ) requires Yi ~ N( ).

a) Thus, I don't need Xi to be normal. 

b) And Yi not being normal => b1 not normal

If I only want to get the coefficients but are not using the p-values, I don't need Yi to be normal.

2) a) E(ei) = 0 and "ei have constant variance and are uncorrelated"

=> b) E(b1) = beta1; and 

   c) "b1 has minimum variance among all unbiased linear estimators of beta1"

If I don't need (2b) and (2c), I don't need (2a).

Am I right?

P.S. Does anyone know how to get the p-values of the coefficients if I am using mAr.est() in R?

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Please either update your question, or post a new one mentioning this one. Answers are for answering. Judging by your text, 2 separate questions can be asked. – mpiktas May 14 '12 at 7:44

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