Stata 15 help for xtabond

[XT] xtabond -- Arellano-Bond linear dynamic panel-data estimation

Syntax

xtabond depvar [indepvars] [if] [in] [, options]

options Description ------------------------------------------------------------------------- Model noconstant suppress constant term diffvars(varlist) already-differenced exogenous variables inst(varlist) additional instrument variables lags(#) use # lags of dependent variable as covariates; default is lags(1) maxldep(#) maximum lags of dependent variable for use as instruments maxlags(#) maximum lags of predetermined and endogenous variables for use as instruments twostep compute the two-step estimator instead of the one-step estimator

Predetermined pre(varlist[...]) predetermined variables; can be specified more than once

Endogenous endogenous(varlist[...]) endogenous variables; can be specified more than once

SE/Robust vce(vcetype) vcetype may be gmm or robust

Reporting level(#) set confidence level; default is level(95) artests(#) use # as maximum order for AR tests; default is artests(2) display_options control spacing and line width

coeflegend display legend instead of statistics ------------------------------------------------------------------------- A panel variable and a time variable must be specified; use xtset; see [XT] xtset. indepvars and all varlists, except pre(varlist[...]) and endogenous(varlist[...]), may contain time-series operators; see tsvarlist. The specification of depvar may not contain time-series operators. by, statsby, and xi are allowed; see prefix. coeflegend does not appear in the dialog box. See [XT] xtabond postestimation for features available after estimation.

Menu

Statistics > Longitudinal/panel data > Dynamic panel data (DPD) > Arellano-Bond estimation

Description

xtabond fits a linear dynamic panel-data model where the unobserved panel-level effects are correlated with the lags of the dependent variable, known as the Arellano-Bond estimator. This estimator is designed for datasets with many panels and few periods, and it requires that there be no autocorrelation in the idiosyncratic errors.

Options

+-------+ ----+ Model +------------------------------------------------------------

noconstant; see [R] estimation options.

diffvars(varlist) specifies a set of variables that already have been differenced to be included as strictly exogenous covariates. diffvars() may not be used for models with a constant or models for which level-equation instruments are specified.

inst(varlist) specifies a set of variables to be used as additional instruments. These instruments are not differenced by xtabond before including them in the instrument matrix.

lags(#) sets p, the number of lags of the dependent variable to be included in the model. The default is p=1.

maxldep(#) sets the maximum number of lags of the dependent variable that can be used as instruments. The default is to use all T_i-p-2 lags.

maxlags(#) sets the maximum number of lags of the predetermined and endogenous variables that can be used as instruments. For predetermined variables, the default is to use all T_i-p-1 lags. For endogenous variables, the default is to use all T_i-p-2 lags.

twostep specifies that the two-step estimator be calculated.

+---------------+ ----+ Predetermined +----------------------------------------------------

pre(varlist [, lagstruct(prelags, premaxlags)]) specifies that a set of predetermined variables be included in the model. Optionally, one may specify that prelags lags of the specified variables also be included. The default for prelags is 0. Specifying premaxlags sets the maximum number of further lags of the predetermined variables that can be used as instruments. The default is to include T_i-p-1 lagged levels as instruments for predetermined variables. You may specify as many sets of predetermined variables as you need within the standard Stata limits on matrix size. Each set of predetermined variables may have its own number of prelags and premaxlags.

+------------+ ----+ Endogenous +-------------------------------------------------------

endogenous(varlist [, lagstruct(endlags, endmaxlags)]) specifies that a set of endogenous variables be included in the model. Optionally, one may specify that endlags lags of the specified variables also be included. The default for endlags is 0. Specifying endmaxlags sets the maximum number of further lags of the endogenous variables that can be used as instruments. The default is to include T_i-p-2 lagged levels as instruments for endogenous variables. You may specify as many sets of endogenous variables as you need within the standard Stata limits on matrix size. Each set of endogenous variables may have its own number of endlags and endmaxlags.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory and that are robust to some kinds of misspecification; see Remarks and examples in [XT] xtabond.

vce(gmm), the default, uses the conventionally derived variance estimator for generalized method of moments estimation.

vce(robust) uses the robust estimator. After one-step estimation, this is the Arellano-Bond robust VCE estimator. After two-step estimation, this is the Windmeijer (2005) WC-robust estimator.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

artests(#) specifies the maximum order of the autocorrelation test to be calculated. The tests are reported by estat abond; see [XT] xtabond postestimation. Specifying the order of the highest test at estimation time is more efficient than specifying it to estat abond, because estat abond must refit the model to obtain the test statistics. The maximum order must be less than or equal to the number of periods in the longest panel. The default is artests(2).

display_options: vsquish and nolstretch; see [R] estimation options.

The following option is available with xtabond but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Examples

Setup . webuse abdata

Basic model with two lags of dependent variable included as regressors . xtabond n l(0/1).w l(0/2).(k ys) yr1980-yr1984, lags(2) . xtabond n l(0/1).w l(0/2).(k ys) yr1980-yr1984, lags(2) vce(robust) . xtabond n l(0/1).w l(0/2).(k ys) yr1980-yr1984, lags(2) twostep

Treat w and k as predetermined and include w, L.w, k, L.k, and L2.k as additional regressors . xtabond n l(0/2).ys yr1980-yr1984, lags(2) pre(w, lag(1,.)) pre(k, lag(2,.))

Treat L.w and L2.k as endogenous and include w, L.w, k, L.k, and L2.k as additional regressors . xtabond n l(0/2).ys yr1980-yr1984, lags(2) endogenous(w, lag(1,.)) endogenous(k, lag(2,.))

Stored results

xtabond stores the following in e():

Scalars e(N) number of observations e(N_g) number of groups e(df_m) model degrees of freedom e(g_min) smallest group size e(g_avg) average group size e(g_max) largest group size e(t_min) minimum time in sample e(t_max) maximum time in sample e(chi2) chi-squared e(arm#) test for autocorrelation of order # e(artests) number of AR tests computed e(sig2) estimate of sigma_epsilon^2 e(rss) sum of squared differenced residuals e(sargan) Sargan test statistic e(rank) rank of e(V) e(zrank) rank of instrument matrix

Macros e(cmd) xtabond e(cmdline) command as typed e(depvar) name of dependent variable e(twostep) twostep, if specified e(ivar) variable denoting groups e(tvar) variable denoting time within groups e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(system) system, if system estimator e(transform) specified transform e(diffvars) already-differenced exogenous variables e(datasignature) checksum from datasignature e(datasignaturevars) variables used in calculation of checksum e(properties) b V e(estat_cmd) program used to implement estat e(predict) program used to implement predict e(marginsok) predictions allowed by margins

Matrices e(b) coefficient vector e(V) variance-covariance matrix of the estimators

Functions e(sample) marks estimation sample

References

Arellano, M., and S. Bond. 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58: 277-297.

Windmeijer, F. 2005. A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics 126: 25-51.


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