**[TS] mgarch ccc postestimation** -- Postestimation tools for mgarch ccc

__Postestimation commands__

The following standard postestimation commands are available after **mgarch**
**ccc**:

Command Description
-------------------------------------------------------------------------
**contrast** contrasts and ANOVA-style joint tests of
estimates
**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
**margins** marginal means, predictive margins, marginal
effects, and average marginal effects
**marginsplot** graph the results from margins (profile plots,
interaction plots, etc.)
**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
**pwcompare** pairwise comparisons of estimates
**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
**xb** linear prediction; the default
__r__**esiduals** residuals
__v__**ariance** conditional variances and covariances
__c__**orrelation** conditional correlations
-------------------------------------------------------------------------
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
__e__**quation(***eqnames***)** names of equations for which predictions are
made
__dyn__**amic(***time_constant***)** begin dynamic forecast at specified time
-------------------------------------------------------------------------

__Menu for predict__

**Statistics > Postestimation**

__Description for predict__

**predict** creates a new variable containing predictions such as linear
predictions and conditional variances, covariances, and correlations.
All predictions are available as static one-step-ahead predictions or as
dynamic multistep predictions, and you can control when dynamic
predictions begin.

__Options for predict__

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

**xb**, the default, calculates the linear predictions of the dependent
variables.

**residuals** calculates the residuals.

**variance** predicts the conditional variances and conditional covariances.

**correlation** predicts the conditional correlations.

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

**equation(***eqnames***)** specifies the equation for which the predictions are
calculated. Use this option to predict a statistic for a particular
equation. Equation names, such as **equation(income)**, are used to
identify equations.

One equation name may be specified when predicting the dependent
variable, the residuals, or the conditional variance. For example,
specifying **equation(income)** causes **predict** to predict **income**, and
specifying **variance equation(income)** causes predict to predict the
conditional variance of income.

Two equations may be specified when predicting a conditional variance
or covariance. For example, specifying **equation(income, consumption)**
**variance** causes **predict** to predict the conditional covariance of
**income** and **consumption**.

**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 **residuals**.

__Syntax for margins__

**margins** [*marginlist*] [**,** *options*]

**margins** [*marginlist*] **,** __pr__**edict(***statistic *...**)** [__pr__**edict(***statistic *...**)**
...] [*options*]

*statistic* Description
-------------------------------------------------------------------------
default linear predictions for each equation
**xb** linear prediction for a specified equation
__v__**ariance** conditional variances and covariances
__c__**orrelation** conditional correlations
__r__**esiduals** not allowed with **margins**
-------------------------------------------------------------------------
**xb** defaults to the first equation.

Statistics not allowed with **margins** are functions of stochastic
quantities other than **e(b)**.

For the full syntax, see **[R] margins**.

__Menu for margins__

**Statistics > Postestimation**

__Description for margins__

**margins** estimates margins of response for linear predictions and
conditional variances, covariances, and correlations. All predictions
are available as static one-step-ahead predictions or as dynamic
multistep predictions, and you can control when dynamic predictions
begin.

__Examples__

Setup
**. webuse stocks**
**. mgarch ccc (toyota nissan = , noconstant) (honda = , noconstant),**
**arch(1) garch(1)**

Forecast conditional variances 50 time periods into the future, using
dynamic predictions beginning in time period 2016, and then graph the
forecasts
**. tsappend, add(50)**
**. predict H*, variance dynamic(2016)**
**. tsline H_toyota_toyota H_nissan_nissan H_honda_honda if t>1600,**
**legend(rows(3)) xline(2015)**