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06-08-2010 03:56 AM

Hello,

I'm doing regression analysis with panel data (small N, large T). I have three related questions and I would be most grateful if anyone could answer at least one of them.

1. Can PROC MIXED estimate one-way fixed/random effects models as in TSCSREG? If so, which settings should one use?

2. I have constructed a mixed effects model. I have a collection of fixed effects plus cross-sectional random effects with autoregressive AR(1) error terms. All is well, but...

Other academic papers that report similar models also report the significance levels associated with

- the covariance parameter estimates; and

- the - 2 log likelihood

The only p-value that I can find is the null model likelihood ratio test. Is this p-value related to one of the above? If not, how will I know how many asterisks to put next to the covariance parameter estimates and the - 2 log likelihood

3. Is it so that the main difference between TSCSREG and MIXED is that they can manage different kinds of error covariance structures. That is, TSCSREG can handle cross-sectional heterogeneity (one-way fixed/random) and contemporaneous correlation (Parks), etc.; and MIXED can handle autoregressive error terms plus random cross-sectional errors (AR(1)), etc.

THANKS!

I'm doing regression analysis with panel data (small N, large T). I have three related questions and I would be most grateful if anyone could answer at least one of them.

1. Can PROC MIXED estimate one-way fixed/random effects models as in TSCSREG? If so, which settings should one use?

2. I have constructed a mixed effects model. I have a collection of fixed effects plus cross-sectional random effects with autoregressive AR(1) error terms. All is well, but...

Other academic papers that report similar models also report the significance levels associated with

- the covariance parameter estimates; and

- the - 2 log likelihood

The only p-value that I can find is the null model likelihood ratio test. Is this p-value related to one of the above? If not, how will I know how many asterisks to put next to the covariance parameter estimates and the - 2 log likelihood

3. Is it so that the main difference between TSCSREG and MIXED is that they can manage different kinds of error covariance structures. That is, TSCSREG can handle cross-sectional heterogeneity (one-way fixed/random) and contemporaneous correlation (Parks), etc.; and MIXED can handle autoregressive error terms plus random cross-sectional errors (AR(1)), etc.

THANKS!

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Posted in reply to deleted_user

06-08-2010 05:37 AM

It seems that I was able to compute the p-values associates with the covariance parameter estimates using the COVTEST option.

However, the significance of the -2 Log Likelihood remains a mystery

However, the significance of the -2 Log Likelihood remains a mystery