help xtdpd dialog: xtdpd
also see: xtdpd postestimation
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Title
[XT] xtdpd -- Linear dynamic panel-data estimation
Syntax
xtdpd depvar [indepvars] [if] [in] , dgmmiv(varlist [...]) [options]
options description
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Model
* dgmmiv(varlist[...]) GMM-type instruments for the difference equation;
can be specified more than once
lgmmiv(varlist[...]) GMM-type instruments for the level equation; can
be specified more than once
iv(varlist[...]) standard instruments for the difference and level
equations; can be specified more than once
div(varlist[...]) standard instruments for the difference equation
only; can be specified more than once
liv(varlist) standard instruments for the level equation only;
can be specified more than once
noconstant suppress constant term
twostep compute the two-step estimator instead of the
one-step estimator
hascons check for collinearity only among levels of
independent variables; by default checks occur
among levels and differences
fodeviation use forward-orthogonal deviations instead of
first differences
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
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* dgmmiv() is required.
+ coeflegend does not appear in the dialog box.
A panel variable and a time variable must be specified; use xtset; see
[XT] xtset.
depvar, indepvars, and all varlists may contain time-series operators;
see tsvarlist.
by, statsby, and xi are allowed; see prefix.
See [XT] xtdpd postestimation for features available after estimation.
Menu
Statistics > Longitudinal/panel data > Dynamic panel data (DPD) >
Linear DPD 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. xtdpd fits a dynamic panel-data model by using
the Arellano-Bond (1991) or the Arellano-Bover/Blundell-Bond (1995, 1998)
estimator.
At the cost of a more complicated syntax, xtdpd can fit models with
low-order moving-average correlation in the idiosyncratic errors or
predetermined variables with a more complicated structure than allowed
for xtabond or xtdpdsys; see [XT] xtabond and [XT] xtdpdsys.
Options
+-------+
----+ Model +------------------------------------------------------------
dgmmiv(varlist [, lagrange(flag [llag])]) specifies GMM-type instruments
for the differenced equation. Levels of the variables are used to
form GMM-type instruments for the difference equation. All possible
lags are used, unless lagrange(flag llag) restricts the lags to begin
with flag and end with llag. You may specify as many sets of
GMM-type instruments for the differenced equation as you need within
the standard Stata limits on matrix size. Each set may have its own
flag and llag. dgmmiv() is required.
lgmmiv(varlist [, lag(#)]) specifies GMM-type instruments for the level
equation. Differences of the variables are used to form GMM-type
instruments for the level equation. The first lag of the differences
is used unless lag(#) is specified, indicating that #th lag of the
differences be used. You may specify as many sets of GMM-type
instruments for the level equation as you need within the standard
Stata limits on matrix size. Each set may have its own lag.
iv(varlist [, nodifference]) specifies standard instruments for both the
differenced and level equations. Differences of the variables are
used as instruments for the differenced equation, unless nodifference
specifies that levels be used. Levels of the variables are used as
instruments for the level equation. You may specify as many sets of
standard instruments for both the differenced and level equations as
you need within the standard Stata limits on matrix size.
div(varlist [, nodifference]) specifies additional standard instruments
for the differenced equation. Specified variables may not be
included in iv() or in liv(). Differences of the variables are used,
unless nodifference is specified, which requests that levels of the
variables be used as instruments for the differenced equation. You
may specify as many additional sets of standard instruments for the
differenced equation as you need within the standard Stata limits on
matrix size.
liv(varlist) specifies additional standard instruments for the level
equation. Specified variables may not be included in iv() or in
div(). Levels of the variables are used as instruments for the level
equation. You may specify as many additional sets of standard
instruments for the level equation as you need within the standard
Stata limits on matrix size.
noconstant; see [R] estimation options.
twostep specifies that the two-step estimator be calculated.
hascons specifies that xtdpd check for collinearity only among levels of
independent variables; by default checks occur among levels and
differences.
fodeviation specifies that forward-orthogonal deviations are to be used
instead of first differences. fodeviation is not allowed when there
are gaps in the data or when lgmmiv() is specified.
+-----------+
----+ 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] xtdpd.
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] xtdpd
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 xtdpd but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Examples
Setup
. webuse abdata
Arellano-Bond estimator with two lags of dependent variable included as
regressors and strictly exogenous covariates
. xtdpd l(0/2).n l(0/1).(w ys) k, dgmmiv(n) div(l(0/1).(w ys) k)
. xtdpd l(0/2).n l(0/1).(w ys) k year yr1980-yr1984, dgmmiv(n)
div(l(0/1).(w ys) k year) div(yr1980-yr1984) nocons hascons
Arellano-Bond estimator with two lags of dependent variable included as
regressors, strictly exogenous covariates and robust VCE
. xtdpd l(0/2).n l(0/1).(w ys) k year yr1980-yr1984, dgmmiv(n)
div(l(0/1).(w ys) k year) div(yr1980-yr1984) nocons hascons
twostep vce(robust)
Arellano-Bover/Blundell-Bond system estimator with two lags of dependent
variable included as regressors and strictly exogenous covariates
. xtdpd l(0/2).n l(0/1).(w ys) k, dgmmiv(n) lgmmiv(n) div(l(0/1).(w
ys) k )
Arellano-Bond estimator with two lags of dependent variable included as
regressors, endogenous covariates and a robust VCE
. xtdpd L(0/1).(n w k) year yr1979-yr1984, dgmmiv(n w k) div(year
yr1979-yr1984) nocons hascons vce(robust)
Arellano-Bover/Blundell-Bond system estimator with two lags of dependent
variable included as regressors, endogenous covariates and a robust VCE
. xtdpd L(0/1).(n w k) year yr1979-yr1984, dgmmiv(n w k) lgmmiv(n w k)
div(year yr1979-yr1984) nocons hascons vce(robust)
Saved results
xtdpd 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(diffvars) already differenced variables
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
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] xtdpd
Help: [XT] xtdpd postestimation;
[XT] xtset; [XT] xtabond, [XT] xtdpdsys, [XT] xtivreg, [XT]
xtreg, [XT] xtregar, [R] gmm
