singular covariance in Kalman filtering

[remuct]

Hi,
I have a concern about a "singular covariance" message which came with the output of a Kalman filter estimation. I am wondering which covariance is concerned here, the covariance of the measurement or of the state equation disturbances or the covariance of the forecasted or updated state?
Maybe this can help: the variable x(t) in my signal eqn y(t)=a(t)x(t)+ u(t) is a weighted average of actual and past values of a variable z(t) such that x(t)= (b1 z(t)+ b2 z(t-1))/(b1+b2). When I specify x(t) this way, I get the message singular covariance. When I specify it equivalently by dividing all parameters by b1, i.e. as x(t) = (z(t) + c z(t-1))/(1+c), I do not have the message anymore. Could it be that the first specification leads to some overidentification problem in the signal eqn, which would explain the singular covariance message of Eviews? And again, what is the covariance concerned?
Thank you for your helpful comments.

[EViews Glenn]

It is the covariance of the coefficients.

It is a little difficult to diagnose without looking at the full specification and data and thinking a lot more than I am doing at present, though I think you are right about overidentification. I will note that in the second (non-singular) parameterization, you are only estimating a single share coefficient instead of the two levels. Which leads me to believe that there is an overidentification problem where the levels are set somewhere else in the specification so all that you can identify is b2/b1.