Stata 15 help for dvech

help dvech dialog: dvech also see: dvech postestimation ------------------------------------------------------------------------------- dvech has been superseded by mgarch dvech. mgarch dvech is part of mgarch, which estimates the parameters of 4 different multivariate GARCH models -- diagonal-vech models, constant conditional-correlation models, dynamic conditional-correlation models, and time-varying conditional-correlation models; thus mgarch dvech does what dvech can do and more. dvech continues to work but, as of Stata 12, is no longer an official part of Stata. This is the original help file, which we will no longer update, so some links may no longer work.


[TS] dvech -- Diagonal vech multivariate GARCH models


dvech eq [eq ... eq] [if] [in] [, options]

where each eq has the form

(depvars = [indepvars], [noconstant])

options Description ------------------------------------------------------------------------- Model arch(numlist) ARCH terms garch(numlist) GARCH terms constraints(numlist) apply linear constraints

SE/Robust vce(vcetype) vcetype may be oim or robust

Reporting level(#) set confidence level; default is level(95) nocnsreport do not display constraints display_options control columns and column formats, row spacing, display of omitted variables and base and empty cells, and factor-variable labeling

Maximization maximize_options control the maximization process; seldom used from(matname) initial values for the coefficients; seldom used svtechnique(algorithm_spec) starting-value maximization algorithm sviterate(#) number of starting-value iterations; default is sviterate(25)

coeflegend display legend instead of statistics ------------------------------------------------------------------------- You must tsset your data before using dvech; see [TS] tsset. indepvars may contain factor variables; see fvvarlist. depvars and indepvars may contain time-series operators; see tsvarlist. by, statsby, and rolling are allowed; see prefix. coeflegend does not appear in the dialog box. See [TS] dvech postestimation for features available after estimation.


Statistics > Multivariate time series > Multivariate GARCH


dvech estimates the parameters of a class of multivariate generalized autoregressive conditional-heteroskedasticity (GARCH) models. Multivariate GARCH models allow the conditional covariance matrix of the dependent variables to follow a flexible dynamic structure. dvech estimates the parameters of diagonal vech GARCH models in which each element of the current conditional covariance matrix of the dependent variables depends only on its own past and on past shocks.


+-------+ ----+ Model +------------------------------------------------------------

noconstant suppresses the constant term(s).

arch(numlist) specifies the ARCH terms in the model. By default, no ARCH terms are specified.

garch(numlist) specifies the GARCH terms in the model. By default, no GARCH terms are specified.

constraints(numlist) specifies linear constraints to apply to the parameter estimates.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the estimator for the variance-covariance matrix of the estimator. vce(oim), the default, specifies to use the observed information matrix (OIM) estimator. vce(robust) specifies to use the Huber/White/sandwich estimator.

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

nocnsreport; see [R] estimation options.

display_options: noomitted, vsquish, noemptycells, baselevels, allbaselevels, cformat(%fmt), pformat(%fmt), and sformat(%fmt); see [R] estimation options.

+--------------+ ----+ Maximization +-----------------------------------------------------

maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), and from(matname); see [R] maximize for all options except from(), and see below for information on from(). These options are seldom used.

from(matname) specifies initial values for the coefficients. from(b0) causes dvech to begin the optimization algorithm with the values in b0. b0 must be a row vector, and the number of columns must equal the number of parameters in the model.

svtechnique(algorithm_spec) and sviterate(#) specify options for the starting-value search process.

svtechnique(algorithm_spec) specifies the algorithm used to search for initial values. The syntax for algorithm_spec is the same as for the technique() option; see [R] maximize. svtechnique(bhhh 5 nr 16000) is the default, and this option may not be specified with from().

sviterate(#) specifies the maximum number of iterations that the search algorithm may perform. The default is sviterate(25), and this option may not be specified with from().

The following option is available with dvech but is not shown in the dialog box:

coeflegend; see [R] estimation options.


--------------------------------------------------------------------------- Setup . webuse irates4

Fit a VAR(1) model of changes in bond and tbill, allowing for ARCH(1) errors . dvech ( D.tbill = LD.tbill), arch(1)

Same as above, but constraining the lagged effect of on D.tbill to be zero and suppressing constraints . dvech ( = LD.tbill, noconstant) /// (D.tbill = LD.tbill, noconstant), arch(1)

--------------------------------------------------------------------------- Setup . webuse acme . constraint 1 [L.ARCH]1_1 = [L.ARCH]2_2 . constraint 2 [L.GARCH]1_1 = [L.GARCH]2_2

Fit a bivariate GARCH model, constraining the two variables' ARCH coefficients to be equal, as well as their GARCH coefficients to be equal . dvech (acme = L.acme) (anvil = L.anvil), arch(1) garch(1) constraints(1 2)

--------------------------------------------------------------------------- Setup . webuse aacmer

Fit a bivariate GARCH model with no regressors or constant terms, including two ARCH terms and one GARCH term . dvech (acme anvil = , noconstant), arch(1/2) garch(1)


Saved results

dvech saves the following in e():

Scalars e(N) number of observations e(k) number of parameters e(k_extra) number of auxiliary parameters e(k_eq) number of equations in e(b) e(k_dv) number of dependent variables e(df_m) model degrees of freedom e(ll) log likelihood e(chi2) chi-squared statistic e(p) significance e(tmin) minimum time in sample e(tmax) maximum time in sample e(rank) rank of VCE e(ic) number of iterations e(converged) 1 if converged, 0 otherwise

Macros e(cmd) dvech e(cmdline) command as typed e(depvars) names of dependent variables e(covariates) list of covariates e(dv_eqs) dependent variables with mean equations e(indeps) independent variables in each equation e(tvar) variable denoting time within groups e(title) title in estimation output e(chi2type) Wald; type of model chi-squared test e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(tmins) formatted minimum time e(tmaxs) formatted maximum time e(arch) specified ARCH terms e(garch) specified GARCH terms e(svtechnique) maximization technique(s) for starting values e(technique) maximization technique e(crittype) optimization criterion 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 e(marginsnotok) predictions disallowed by margins

Matrices e(b) coefficient vector e(Cns) constraints matrix e(ilog) iteration log (up to 20 iterations) e(gradient) gradient vector e(hessian) Hessian matrix e(A) estimates of A matrices e(B) estimates of B matrices e(S) estimates of Sigma0 matrix e(Sigma) Sigma hat e(pinfo) parameter information, used by predict e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample

Also see

Manual: previously documented

Help: [TS] dvech postestimation; [TS] arch, [TS] var

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