## Stata 15 help for dfactor_postestimation

```
[TS] dfactor postestimation -- Postestimation tools for dfactor

Postestimation commands

The following standard postestimation commands are available after
dfactor:

Command            Description
-------------------------------------------------------------------------
estat ic           Akaike's and Schwarz's Bayesian information criteria
(AIC and BIC)
estat summarize    summary statistics for the estimation sample
estat vce          variance-covariance matrix of the estimators (VCE)
estimates          cataloging estimation results
forecast           dynamic forecasts and simulations
lincom             point estimates, standard errors, testing, and
inference for linear combinations of coefficients
lrtest             likelihood-ratio test
nlcom              point estimates, standard errors, testing, and
inference for nonlinear combinations of coefficients
predict            predictions, residuals, influence statistics, and
other diagnostic measures
predictnl          point estimates, standard errors, testing, and
inference for generalized predictions
test               Wald tests of simple and composite linear hypotheses
testnl             Wald tests of nonlinear hypotheses
-------------------------------------------------------------------------

Syntax for predict

predict [type] {stub*|newvarlist} [if] [in] [, statistic options]

statistic         Description
-------------------------------------------------------------------------
Main
y                dependent variable, which is xbf + residuals
xb               linear predictions using the observable independent
variables
xbf              linear predictions using the observable independent
variables plus the factor contributions
factors          unobserved factor variables
residuals        autocorrelated disturbances
innovations      innovations, the observed dependent variable minus the
predicted y
-------------------------------------------------------------------------
These statistics are available both in and out of sample; type predict
... if e(sample) ... if wanted only for the estimation sample.

options                    Description
-------------------------------------------------------------------------
Options
equation(eqnames)         specify name(s) of equation(s) for which
predictions are to be made
rmse(stub*|newvarlist)    put estimated root mean squared errors of
predicted objects in new variables
dynamic(time_constant)    begin dynamic forecast at specified time

smethod(method)           method for predicting unobserved states
-------------------------------------------------------------------------

method             Description
-------------------------------------------------------------------------
onestep            predict using past information
smooth             predict using all sample information
filter             predict using past and contemporaneous information
-------------------------------------------------------------------------

Statistics > Postestimation

Description for predict

predict creates a new variable containing predictions such as expected
values, unobserved factors, autocorrelated disturbances, and innovations.
The root mean squared error is available for all predictions.  All
predictions are also available as static one-step-ahead predictions or as
dynamic multistep predictions, and you can control when dynamic
predictions begin.

Options for predict

The mathematical notation used in this section is defined in Description
of [TS] dfactor.

+------+
----+ Main +-------------------------------------------------------------

y, xb, xbf, factors, residuals, and innovations specify the statistic to
be predicted.

y, the default, predicts the dependent variables. The predictions
include the contributions of the unobserved factors, the linear
predictions by using the observable independent variables, and
any autocorrelation.

xb calculates the linear prediction by using the observable
independent variables.

xbf calculates the contributions of the unobserved factors plus the
linear prediction by using the observable independent variables.

factors estimates the unobserved factors.

residuals calculates the autocorrelated residuals.

innovations calculates the innovations.

+---------+
----+ Options +----------------------------------------------------------

equation(eqnames) specifies the equation(s) for which the predictions are
to be calculated.

You specify equation names, such as equation(income consumption) or
equation(factor1 factor2), to identify the equations.  For the
factors statistic, you must specify names of equations for factors;
for all other statistics, you must specify names of equations for
observable variables.

If you do not specify equation() and do not specify stub*, the
results are the same as if you had specified the name of the first
equation for the predicted statistic.

equation() may not be specified with stub*.

rmse(stub*|newvarlist) puts the root mean squared errors of the predicted
objects into the specified new variables.  The root mean squared
errors measure the variances due to the disturbances but do not
account for estimation error.

dynamic(time_constant) specifies when predict starts producing dynamic
forecasts.  The specified time_constant must be in the scale of the
time variable specified in tsset, and the time_constant must be
inside a sample for which observations on the dependent variables are
available.  For example, dynamic(tq(2008q4)) causes dynamic
predictions to begin in the fourth quarter of 2008, assuming that
your time variable is quarterly, see [D] datetime.  If the model
contains exogenous variables, they must be present for the whole
predicted sample.  dynamic() may not be specified with xb, xbf,
innovations, smethod(filter), or smethod(smooth).

+----------+

smethod(method) specifies the method used to predict the unobserved
states in the model.  smethod() may not be specified with xb.

smethod(onestep), the default, causes predict to use previous
information on the dependent variables.  The Kalman filter is
performed on previous periods, but only the one-step predictions
are made for the current period.

smethod(smooth) causes predict to estimate the states at each time
period using all the sample data by the Kalman smoother.

smethod(filter) causes predict to estimate the states at each time
period using previous and contemporaneous data by the Kalman
filter.  The Kalman filter is performed on previous periods and
the current period.  smethod(filter) may be specified only with
factors and residuals.

Examples

Setup
. webuse dfex
. dfactor (D.(ipman income hours unemp) = , noconstant ar(1)) (f = ,
ar(1/2))

Forecast changes in ipman 6 months into the future, using dynamic
predictions starting in December 2008, and then graph the series