help xtdpdsys dialog: xtdpdsys
also see: xtdpdsys postestimation
-------------------------------------------------------------------------------
Title
[XT] xtdpdsys -- Arellano-Bover/Blundell-Bond linear dynamic panel-data
estimation
Syntax
xtdpdsys depvar [indepvars] [if] [in] [, options]
options description
-------------------------------------------------------------------------
Model
noconstant suppress constant term
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
+ coeflegend display coefficients' legend instead of
coefficient table
-------------------------------------------------------------------------
+ coeflegend does not appear in the dialog box.
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.
See [XT] xtdpdsys postestimation for features available after estimation.
Menu
Statistics > Longitudinal/panel data > Dynamic panel data (DPD) >
Arellano-Bover/Blundell-Bond estimation
Description
Linear dynamic panel-data models include p lags of the dependent variable
as covariates and contain unobserved panel-level effects, fixed or
random. By construction, the unobserved panel-level effects are
correlated with the lagged dependent variables, making standard
estimators inconsistent. Arellano and Bond (1991) derived a consistent
generalized method of moments (GMM) estimator for this model. The
Arellano and Bond estimator can perform poorly if the autoregressive
parameters are too large or the ratio of the variance of the panel-level
effect to the variance of idiosyncratic error is too large. Building on
the work of Arellano and Bover (1995), Blundell and Bond (1998) developed
a system estimator that uses additional moment conditions; xtdpdsys
implements this estimator.
This estimator is designed for datasets with many panels and few periods.
This method assumes that there is no autocorrelation in the idiosyncratic
errors and requires the initial condition that the panel-level effects be
uncorrelated with the first difference of the first observation of the
dependent variable.
Options
+-------+
----+ Model +------------------------------------------------------------
noconstant; see [R] estimation options.
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 variables
that can be used as instruments. The default is to use all T_i-p-2
lags of the dependent variable. If the predetermined variables are
endogenous, the default is to use all T_i-p-2 lags of these
endogenous variables. If the predetermined variables are not
endogenous, the default is to use all T_i-p-1 lags of these
variables.
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 [XT] xtdpdsys.
vce(gmm), the default, uses the conventionally derived variance
estimator for generalized method of moments estimation.
vce(robust) uses the robust estimator. For the one-step estimator,
this is the Arellano-Bond robust VCE estimator. For the two-step
estimator, this is the Windmeijer 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] xtdpdsys
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; see [R] estimation options.
The following option is available with xtdpdsys but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Examples
Setup
. webuse abdata
Basic model with strictly exogenous covariates and two lags of the the
dependent variable
. xtdpdsys n l(0/1).w l(0/2).(k ys) yr1980-yr1984, lags(2)
Same model with a robust VCE
. xtdpdsys n l(0/1).w l(0/2).(k ys) yr1980-yr1984, lags(2)
vce(robust)
Twostep estimator of the same model with a robust VCE
. xtdpdsys n l(0/1).w l(0/2).(k ys) yr1980-yr1984, lags(2) twostep
vce(robust)
Now allow some of the covariates to be predetermined
. xtdpdsys n l(0/1).w l(0/2).(k ys) yr1980-yr1984, lags(2) twostep
pre(w, lag(1,.)) pre(k,lag(2,.))
Now allow some of the covariates to be endogenous
. xtdpdsys n l(0/1).ys yr1980-yr1984, lags(2) twostep endogenous(w,
lag(1,.)) endogenous(k,lag(2,.))
Saved results
xtdpdsys saves 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) model chi-squared statistic
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) xtdpd
e(cmdline) command as typed
e(depvar) name of dependent variable
e(twostep) twostep, if specified
e(ivar) variable denoting groups
e(tvar) time variable
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(system) system, if system estimator
e(hascons) hascons, if specified
e(transform) specified transform
e(engine) xtdpd
e(div_odvars) differenced variables used as standard instruments
for differenced equation and not for level
equation
e(div_olvars) level variables used as standard instruments for
differenced equation and not for level equation
e(liv_olvars) level variables used as standard instruments for
level equation and not for differenced equation
e(div_dvars) differenced variables used as standard instruments
for differenced equation
e(div_lvars) level variables used as standard instruments for
differenced equation
e(liv_lvars) level variables used as standard instruments for
level equation
e(dgmmiv_vars) variables used to create GMM-type instruments for
differenced equation
e(dgmmiv_flag) first lags of variables used to create GMM-type
instruments for differenced equation
e(dgmmiv_llag) last lags of variables used to create GMM-type
instruments for differenced equation
e(lgmmiv_vars) variables used to create GMM-type instruments for
level equation
e(lgmmiv_lag) lag used to create GMM-type instruments for level
equation
e(datasignature) checksum from datasignature
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
Results e(div_odvars), e(div_olvars), e(liv_olvars), e(div_dvars),
e(div_lvars), e(liv_lvars), e(dgmmiv_vars), e(dgmmiv_flag),
e(dgmmiv_llag), e(lgmmiv_vars), and e(lgmmiv_lag) describe the
instruments used by xtdpdsys. These results are rarely of interest; see
the options of [XT] xtdpd for more details.
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.
Arellano, M., and O. Bover. 1995. Another look at the instrumental
variable estimation of error-components models. Journal of
Econometrics 68: 29-51.
Blundell, R., and S. Bond. 1998. Initial conditions and moment
restrictions in dynamic panel data models. Journal of Econometrics
87: 115-143.
Also see
Manual: [XT] xtdpdsys
Help: [XT] xtdpdsys postestimation;
[XT] xtset; [XT] xtabond, [XT] xtdpd, [XT] xtivreg, [XT] xtreg,
[XT] xtregar
