Title

xtdynestimb postestimation -- Postestimation tools for
xtdynestimb

(part of xtdynestimb.)

Postestimation commands

After any xtdynestimb estimation the following standard tools are
available, because the command posts e(b) and e(V):

command

xtdynestimb graph coefficient plot

predict linear prediction or residuals (in levels)

test, testnl Wald tests of coefficients

lincom, nlcom linear / nonlinear combinations (e.g.
long-run effects)

estimates store, restore and compare models

xtdyntest specification tests on the residuals (companion
package)

predict

predict [type] newvar [if] [in] [, statistic]

statistic

xb linear prediction in levels (the default)

residuals residuals in levels, depvar - xb

The prediction uses the level-equation coefficients, so the residuals are in the
original scale of depvar and are suitable as input to the residual-based
cross-sectional-dependence tests in xtdyntest.

Coefficient plot

xtdynestimb graph (or the graph option at estimation) draws a
dependency-free plot of the point estimates with confidence intervals:

. xtdynestimb dd n, lags(1)

. xtdynestimb graph, name(mycoef)

Long-run effects

For an AR(1) model the long-run multiplier of a regressor x is
b_x / (1 - a) where a is the persistence coefficient. Use
nlcom:

. xtdynestimb dd n w, lags(1)

. nlcom (lr_w: _b[w]/(1-_b[L1.n]))

For the persistence half-life or cumulative dynamics with lags(2), combine
the lag coefficients similarly inside nlcom.

Specification tests (companion package)

Because predict, residuals returns level residuals, the
xtdyntest battery can be run directly:

. xtdynestimb csdgmm n w, variant(system)

. predict double e, residuals

. xtdyntest csd, residuals(e) // Pesaran CD, Frees, Friedman, BP-LM

. xtdyntest lee n, residuals(e) // neglected-nonlinearity test

Author

Dr Merwan Roudane

merwanroudane920@gmail.com

github.com/merwanroudane