ABAR: Stata module to perform Arellano-Bond test for autocorrelation

abar performs the Arellano-Bond (1991) test for autocorrelation. The test was originally proposed for a particular linear Generalized Method of Moments dynamic panel data estimator, but is quite general in its applicability--more general than dwstat, durbina, bgodfrey, and xtserial. It can be applied to linear GMM regressions in general, and thus to ordinary least squares (OLS) and two-stage least-squares (2SLS) regressions, which can be seen as special cases of linear GMM. It is appropriate for both time-series and cross-section time-series (panel) regressions. It can also be made consistent in the presence of various patterns of error covariance. Specifically, abar will run after regress, ivreg, ivreg2, and ivreg2, gmm in their "plain" (homoskedastic), robust, and cluster variants. It will also run after newey and newey2. In the context of an Arellano-Bond GMM regression, which is run on first differences, AR(1) is to be expected, and therefore the Arellano-Bond AR(1) test result is usually ignored in that context. The AR(2) test on the residuals in first differences is used to detect AR(1) in the underlying levels variables. But in other contexts, such as simple OLS in levels, the AR(1) test is relevant. abar is not appropriate for fixed-effects regressions for dynamic models, assuming those are done via a mean-deviation transformation. This is because the Arellano-Bond AR() test assumes that right-hand-side variables are not "post-determined," i.e., not correlated with future errors. In a dynamic setting, future values of regressors can depend on future errors. And after the mean-deviations transformation, future values of the original regressors affect current values of the transformed versions.