VECM Causality
Posted: Wed Nov 07, 2012 3:26 am
Hi there!
Apologies for cross-posting, this may have previously been on an incorrect board.
I was wondering if there was a simple way of jointly testing long and short-run causality in VECM models in Eviews - that is (obviously where r=1!): assume Y=(a,b), \deltaY(t)=\alpha\beta(Y(-1)) +\gamma\delta Y(-1)+\gamma\delta(-2)+...+epsilon.
Under accepted definitions of VECM causality testing (e.g. mosconi and giannini 1992), we want to jointly test - for example, a causing b: the short-run causality by a standard wald test on \gamma_{a,1}=...=\gamma_{a,k-1}=0 in the second equation of the system, and \alpha_2\beta_1=0 (or, alternatively, \alpha1.\alpha2, for variable 2 causing variable 1, with all of the coefficients on the difference terms of variable 2 in the first equation being jointly equal to zero and \alpha2.\beta1 for variable 1 causing variable2, with all the coefficients on the difference terms of variable 1 in the second equation being jointly equal to zero)?
Sorry if I have misinterpretted the issue, but it is how i understand the issue from the literature such as:
http://web.uvic.ca/~jaclarke/clarkemirza_2006.pdf
What I am essentially asking is if there is a way to jointly test the (non-linear) restriction (for example) such as:
alpha_1*beta_2 = 0 (in the cointegrating vector and the error correction mechanism respectively)
AND
gamma_{2,t-1}=...=gamma_{2,t-k}=0, where gammas are the coefficients on the lagged (endogenous) variables.
Thanks
Apologies for cross-posting, this may have previously been on an incorrect board.
I was wondering if there was a simple way of jointly testing long and short-run causality in VECM models in Eviews - that is (obviously where r=1!): assume Y=(a,b), \deltaY(t)=\alpha\beta(Y(-1)) +\gamma\delta Y(-1)+\gamma\delta(-2)+...+epsilon.
Under accepted definitions of VECM causality testing (e.g. mosconi and giannini 1992), we want to jointly test - for example, a causing b: the short-run causality by a standard wald test on \gamma_{a,1}=...=\gamma_{a,k-1}=0 in the second equation of the system, and \alpha_2\beta_1=0 (or, alternatively, \alpha1.\alpha2, for variable 2 causing variable 1, with all of the coefficients on the difference terms of variable 2 in the first equation being jointly equal to zero and \alpha2.\beta1 for variable 1 causing variable2, with all the coefficients on the difference terms of variable 1 in the second equation being jointly equal to zero)?
Sorry if I have misinterpretted the issue, but it is how i understand the issue from the literature such as:
http://web.uvic.ca/~jaclarke/clarkemirza_2006.pdf
What I am essentially asking is if there is a way to jointly test the (non-linear) restriction (for example) such as:
alpha_1*beta_2 = 0 (in the cointegrating vector and the error correction mechanism respectively)
AND
gamma_{2,t-1}=...=gamma_{2,t-k}=0, where gammas are the coefficients on the lagged (endogenous) variables.
Thanks