help pvar
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Title
pvar -- Panel vector autoregressive models
Syntax
pvar depvarlist [if] [in] [, options]
options Description
----------------------------------------------------------------------------------------
Model
lags(#) use first # lags in the underlying pVAR; default is
lags(1)
exog(varlist) use time-varying exogenous variables in varlist
*fod use Helmert transformation to remove panel-specific fixed
effects; the default
*fd use first difference to remove panel-specific fixed
effects
Model 2
td remove cross-sectional mean from each variable in
depvarlist and in varlist if specified
instlags(numlist) specify lag orders of depvarlist to be used as instruments
gmmstyle use "GMM-style" instruments; may only be used with
instlags()
gmmopts(options) override the default GMM options
SE/Robust
vce(vcetype[, independent]) vcetype may be robust, cluster clustervar, bootstrap,
jackknife, hac kernel lags or unadjusted; default is
vce(unadjusted)
Reporting
overid report Hansen's J statistic of overidentying restrictions
level(#) set confidence level; default is level(95)
noprint do not display coefficient table
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You must xtset your data before using pvar; see [XT] xtset.
Description
pvar fits a multivariate panel regression of each dependent variable on lags of itself
and on lags of all other dependent variables using generalized method of moments (GMM).
pvar also fits a variant of panel vector autoregressive models (pVAR) known as pVARX,
which also includes exogenous explanatory variables. See [R] gmm for GMM estimation
option details.
Options
+-------+
----+ Model +---------------------------------------------------------------------------
lags(#) specifies the maximum lag order # to be included in the model. The default is
to use the first lag of each variable in depvarlist.
exog(varlist) specifies a list of exogenous variables to be included in the pVAR.
fod specifies that the panel-specific fixed effects be removed using forward orthogonal
deviation or Helmert transformation. By default, the first # lags of depvarlist in
the model are instrumented by the same lags. This is the default option.
fd specifies that the panel-specific fixed effects be removed using first difference
instead of forward orthogonal deviations. By default, the first # lags of depvarlist
in the model are instrumented by the #+1 to 2#+1 lags of depvarlist.
+---------+
----+ Model 2 +-------------------------------------------------------------------------
td specifies that the cross-sectional mean be removed by differencing from each series.
instlags(numlist) overrides the default lag orders of depvarlist used as instruments in
the model. Instead the numlist-th lags are used as instruments.
gmmstyle specifies that "GMM-style" instruments as proposed by Holtz-Eakin, Newey and
Rosen (1988) be used. For each instrument based on lags of depvarlist, missing
values are substituted with zero. Observations with no valid instruments are
excluded.
gmmopts(options) overrides the default pvar options. Equations in the model are named
using each variable in depvarlist. See [R] gmm for options.
vce(vcetype[, independent]) specifies the type of standard error reported, which
includes types that are robust to some types of misspecification, that allow for
intragroup correlation, and that use bootstrap or jackknife methods; see [R]
vce_option.
overid specifies that Hansen's J statistic of overidentifying restriction be reported.
This option is available only for over-identified systems.
level(#); see [R] estimation_options.
noprint suppresses printing of the coefficient table.
Remarks
This version is in beta mode. No warranties whatsoever.
Examples
Setup
. webuse nlswork2
. xtset idcode year
. gen wage = exp(ln_wage)
Fit panel vector autoregressive model with 1 lag by Helmert transformation (the default)
. pvar wage hours
Same as above but with standard errors clustered by industry-occupation
. egen indocc = group(ind_code occ_code)
. pvar wage hours, vce(cluster indocc)
Same as first, but use the first three lags as instruments
. pvar wage hours, instl(1/3)
Same as above, but use "GMM-style" instruments
. pvar wage hours, instl(1/3) gmms
Same as above, but report over-identification test
. pvar wage hours, instl(1/3) gmms overid
Fit default pvar options using gmmopts(options)
. pvar wage hours, gmmo(winitial(identity) wmatrix(robust) twostep vce(unadjusted))
Saved results
pvar saves the following in e():
Scalars
e(N) number of observations
e(n) number of panels
e(tmin) first time period in sample
e(tmax) last time period in sample
e(tbar) average time periods among panels
e(mlag) maximum lag order in pVAR
e(N_clust) number of clusters
e(Q) criterion function
e(J) Hansen's J chi-squared statistic
e(J_df) J statistic degrees of freedom
e(rank) rank of e(V)
e(ic) number of iterations used by iterative GMM estimator
e(converged) 1 if converged, 0 otherwise
Macros
e(cmd) pvar
e(cmdline) command as typed
e(depvar) names of dependent variables
e(exog) names of exogenous variables, if specified
e(clustvar) name of cluster variable
e(instr) instruments
e(eqnames) equation names
e(timevar) name of time variable
e(panelvar) name of panel variable
e(properties) b V
Matrices
e(b) coefficient vector
e(V) Variance-covariance matrix of the estimator
e(Sigma) Variance-covariance matrix of the model residuals
e(W) weight matrix used for final round of estimation
e(init) initial values of the estimators
Functions
e(sample) mark estimation sample
References
Alvarez, J. and M. Arellano (2003). The time-series and cross-section asymptotics of
dynamic panel data estimators. Econometrica, 71(4), 1121-1159.
Holtz-Eakin, D., W. Newey and H.S. Rosen (1988). Estimating vector autoregressions with
panel data. Econometrica, 56(6), 1371-1395.
Also see
Help: pvarirf, pvarfevd, pvargranger, pvarsoc, pvarstable
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