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Need help about this, could you please detail the univariate case used for initial residual covariance ?
Is it a simple AR(p) on each endogenous variable (p being endogenous lags)?

Yes, sigma_i^2s are the variances of residuals from univariate pth order autoregressions.

EViews specifies the Litterman hyperparameters to be lambda1>0, lambda2 in [0,1] and lambda3>0. sigma_i and sigma_j are calculated by your choice of the initial covariance options: univariate AR, diagonal VAR, or full VAR estimates. According to your hyperparameters specification, EViews sets the co...

Hi Adina, Yes, EViews implemented the conjugate Normal-Wishart prior and derived the analytical posterior results. The current EViews allows you to specify only 2 hyperparameters, mu1 and lambda1, for the coefficients Normal prior. The Wishart prior values are fixed as S = identity (scale matrix) an...

The variance of the coefficients is assumed to have the form with the *three* hyperparameters (please see the attachment).

Can you provide me your Matlab script and the name of the toolbox(es) required for the script? I would like to run the script to replicate the result.

Can you upload your workfile so that I can replicate the error?

Can you download the CDtest addin from our website (http://www.eviews.com/Addins/addins.shtml)? Please let me know if you still see the problem.

Can you be more specific?

When you download the CDtest addin, EViews will lead you to save the CDtest addin in your *EViews Addins* (default) folder. Can you see the following four files in your *CDtest* folder?

There were computational issues because the given panel data is severely unbalanced (especially, *T<4*). Please reinstall your cdtest addin, and then you will see the following table with a warning message*. The table shows the NA* value for the Frees test because computation of the appropriate quan...

Below is a subroutine to produce a random draw from the specified Wishart distribution. 'command: wish(S, h, n) -- generates multivariate Wishart random variables 'Inputs: h --- m-by-m scale matrix ' n --- scalar degree of freedom 'Outputs: S = m-by-m matrix draw from the Wishart distribution 'Note:...

Below is a subroutine to produce a sample from the specified multivariate normal distribution. Comments are welcomed. 'command: vector(k) y; vector(k) mean; sym(k) cov; mvrnd(y, mean, cov) will generate a multivariate normal random variate y 'Inputs: mean --- k-by-1 mean vector ' cov --- k-by-k symm...

I am very confused about your question. Are you trying to read your data from your ASCII file and create a new series which contains specific scalar values? read(t=dat) input.txt 2 will read data from an ASCII file *input.txt* in the default directory and show you 2 series, which are named to be SER...

This implies that your dataset is too large so that the SLM test cannot be performed. I would suggest to exclude the SLM test from the list for now.

I will figure out how to deal with the large dataset problem.

Can you be more specific? Are you trying to generate a dummy (e.g. 1 if x \in 0.25%, 0 otherwise), or trying to sort out your data?

Standard deviations of residuals. If errors~N(0,1), then the dashed lines would be -1 and 1.