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help for abar
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Arellano-Bond test for autocorrelation

        abar [if exp] [in range] [, lags(#)]

    abar is for use after regress, ivreg, ivreg2, ivregress, newey, and 
    newey2.

    abar is for use with cross-section time-series data.  You must tsset your
    data before using abar.

Description

    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 the
    special cases of ordinary least squares (OLS) and two-stage least-squares
    (2SLS).  It can also be made consistent in the presence of various
    patterns of error covariance.  Specifically, abar will run after regress,
    ivreg, ivreg2, ivreg2, gmm2s, and ivregress 2sls in their "plain"
    (homoskedastic), robust, and cluster variants.  It will also run after
    regressions using the Bartlett/Newey-West kernel, whether performed by 
    newey, newey2, ivreg2, or ivregress.

    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.

    The optional if and in clauses allow one to test for autocorrelation in a
    subset of the residuals.

Citation
    abar is not an official Stata command.  It is a free contribution to the
        research community.  Please cite it as such:
        Roodman, D. 2006. How to Do xtabond2: An Introduction to "Difference"
        and "System" GMM in Stata. Working Paper 103. Center for Global
        Development, Washington.

Options

    lags(#) specifies the maximum number of lags over which to test for AR()
        autocorrelation.  abar will perform separate tests for each lag up to
        the maximum.  The default is 1.

References

    Arellano, M. and S. Bond. 1991.  Some tests of specification for panel
        data: Monte Carlo evidence and an application to employment
        equations. The Review of Economic Studies 58: 277-97.
    Roodman, D. 2006. How to Do xtabond2: An Introduction to "Difference" and
        "System" GMM in Stata. Working Paper 103. Center for Global
        Development, Washington.

Examples
    . use http://www.stata-press.com/data/r7/abdata.dta
    . * Make a close match with first regression (OLS) in abest2.out, in
        http://www.nuff.ox.ac.uk/Users/Doornik/software/dpdox121.zip
    . * Differs because Stata's regress does small-sample correction.
    . regress n L(1/2).n L(0/1).w L(0/2).(k ys) yr1979-yr1984, cluster(id)
    . abar, lags(2)
    . * Now do a perfect match with last regression (2SLS, Anderson-Hsiao) in
        same file.
    . ivreg2 D.n DL2.n DL(0/1).w DL(0/2).(k ys) yr1980-yr1984 (DL.n = L3.n),
        cluster(id)
    . abar, lags(2)
    . * Now do same via xtabond2
    . xtabond2 D.n DL(1/2).n DL(0/1).w DL(0/2).(k ys) y*98*,iv(L3.n DL2.n
        DL(0/1).w DL(0/2).(k ys) y*98*,eq(lev)) rob arle



Author

    David Roodman, Center for Global Development, Washington, DC
    Center for Global Development
    Washington, DC
    droodman@cgdev.org

Also see

    Online: help for regress, ivreg, ivreg2, dwstat, durbina, bgodfrey, 
             xtserial, xtabond2.