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Hello,   I run your model using the sample data you provided. Here are few suggestions that you might want to try.   1. FIXTWO option has no impact on estimating the GMM model. Basically it just estimates FIXTWO model in addition to GMM one. 2. DEPVAR(LEVEL) and NOLEVELS options are conflicting. DEPVAR(LEVAL) says to use levels as instruments while NOLEVELS is saying to ignore it. In the end NOLEVELS is ignored and it estimates the model with levels as instruments only.  3. Is this the correct model you want to estimate with levels as instruments only? You might want to use differenced equations too. See the DEPVAR option in SAS/ETS documentation. 4. The first step is estimable (see this by removing TWOSTEP option) but using your sample data there is a problem with residuals. Is this model parametrized correctly?  5. Using the sample data the model fit is pretty bad (t-values close to zero). Likely that this parametrization is causing problems in 4.   Jan   ... View more

Your test is rejecting the null which means that the errors are either serially correlated or/and the cross sectional effects are not independent. Parks method might not be needed but you might want to fit an AR/MA model. For discussion of these models see for example Baltagi, "Econometric Analysis of Panel Data". Unfurtunatelly, aside from dynamic panel estimator, Parks and Dasilva, the PANEL procedure doesn't accomodate these models. You might be able to fit some of them in the SSM procedure.    ... View more

The Parks method might be a good choice but it really depends on diagnostics and assumptions behing your model. It is a good idea to test first if the errors are autocorrelated. The PANEL procedure offers several tests. Durbin-Watson BFN or Baltagi and Li's tests might be a good start. I would try to run several tests and figure out what type of correlation I have because it determines what type of model I might want to fit. If the errors are autocorrelated, the modeling possibilities range from differencing the data, Arellano and Bond's dynamic panel estimators or Park's method. The Park's model is typically used if you want to include a structure for contemporaneous correlation between cross sections with possible heteroscedasticity on top of autocorrelated errors.     If the panel is nonstationary you might want to difference the data. DaSilva is an MA type of estimator that doesn't help with this problem.   Hadri's test: Under the null data is stationary. Your test rejects the null and therefore the panel is nonstationary.   CD test: Under the null the residuals are not correlated. Your  p-value is pretty high indicating that the null cannot be rejected. There is no contemporaneous correlation between cross sections which somewhat suggests that the Parks model might not be needed. ... View more

Depending on the model you are trying to estimate you might be able to find it in SAS/ETS. Below is a link to the SAS/ETS documentation: http://support.sas.com/documentation/cdl/en/etsug/66840/PDF/default/etsug.pdf See the PANEL procedure for one way and two way random effect models and the Parks method. MODEL and SYSLIN procedures could be also helpful. ... View more