Hi,
I am trying to estimate the MIDAS models of clements and galvao (2008) in order to estimate GDP growth using the leading indicators.
The leading indicator I am using to estimate gdp growth is new building permits and the equation i am trying to estimate is equation 3 in the attached page; with m=3, K=5 and h=1.
My code to estimate this in eviews is:
ls gdpg c(3) c(5)*gdpg(-1) c(4)*((1/(exp(c(1)+c(2))+exp((2*c(1))+(4*c(2)))+exp((3*c(1))+(9*c(2)))+exp((4*c(1))+(16*c(2)))+exp((5*c(1))+(25*c(2)))))*exp(c(1)+c(2))*(permitg(-1)-c(5)*permitg(-2))+(1/(exp(c(1)+c(2))+exp((2*c(1))+(4*c(2)))+exp((3*c(1))+(9*c(2)))+exp((4*c(1))+(16*c(2)))+exp((5*c(1))+(25*c(2)))))*exp((2*c(1))+(4*c(2)))*(permitg1(-1)-c(5)*permitg1(-2))+(1/(exp(c(1)+c(2))+exp((2*c(1))+(4*c(2)))+exp((3*c(1))+(9*c(2)))+exp((4*c(1))+(16*c(2)))+exp((5*c(1))+(25*c(2)))))*exp((3*c(1))+(9*c(2)))*(permitg2(-1)-c(5)*permitg2(-2))+(1/(exp(c(1)+c(2))+exp((2*c(1))+(4*c(2)))+exp((3*c(1))+(9*c(2)))+exp((4*c(1))+(16*c(2)))+exp((5*c(1))+(25*c(2)))))*exp((4*c(1))+(16*c(2)))*(permitg(-2)-c(5)*permitg(-3))+(1/(exp(c(1)+c(2))+exp((2*c(1))+(4*c(2)))+exp((3*c(1))+(9*c(2)))+exp((4*c(1))+(16*c(2)))+exp((5*c(1))+(25*c(2)))))*exp((5*c(1))+(25*c(2)))*(permitg1(-2)-c(5)*permitg1(-3)))
where gdpg is measured, permitg, permitg1 and permitg2 are measured quarterly. permitg1 is the the month prior to permitg and permitg2 is 2 months prior to permitg.
My problem is that when I try to run this regression i am greeted by the "Near singular matrix" error message.
Any help on this would be much appreciated.
Thanks
Mixed Data Sampling "Near Singular Matrix"
Moderators: EViews Gareth, EViews Moderator
Mixed Data Sampling "Near Singular Matrix"
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EViews Gareth
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Re: Mixed Data Sampling "Near Singular Matrix"
You've probably got perfectly collinear regressors. Obviously it is pretty difficult to diagnose what is causing the collinearity with such a large set of regressors.
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startz
- Non-normality and collinearity are NOT problems!
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Mixed Data Sampling "Near Singular Matrix"
It is also possible that multicollinearity is a consequence of the coefficient starting values.
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EViews Gareth
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Re: Mixed Data Sampling "Near Singular Matrix"
that's a good point too.
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