Stata 15 help for mgarch dvech

[TS] mgarch dvech -- Diagonal vech multivariate GARCH models


mgarch 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 distribution(dist [#]) use dist distribution for errors [may be gaussian (synonym normal) or t; default is gaussian] 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, line width, 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 mgarch dvech; see [TS] tsset. indepvars may contain factor variables; see fvvarlist. depvars and indepvars may contain time-series operators; see tsvarlist. by, fp, rolling, and statsby are allowed; see prefix. coeflegend does not appear in the dialog box. See [TS] mgarch dvech postestimation for features available after estimation.


Statistics > Multivariate time series > Multivariate GARCH


mgarch dvech estimates the parameters of diagonal vech (DVECH) multivariate generalized autoregressive conditionally heteroskedastic (MGARCH) models in which each element of the conditional correlation matrix is parameterized as a linear function of its own past and past shocks.

DVECH MGARCH models are less parsimonious than the conditional correlation models discussed in [TS] mgarch ccc, [TS] mgarch dcc, and [TS] mgarch vcc because the number of parameters in DVECH MGARCH models increases more rapidly with the number of series modeled.


+-------+ ----+ 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.

distribution(dist [#]) specifies the assumed distribution for the errors. dist may be gaussian, normal, or t.

gaussian and normal are synonyms; each causes mgarch dvech to assume that the errors come from a multivariate normal distribution. # cannot be specified with either of them.

t causes mgarch dvech to assume that the errors follow a multivariate Student t distribution, and the degree-of-freedom parameter is estimated along with the other parameters of the model. If distribution(t #) is specified, then mgarch dvech uses a multivariate Student t distribution with # degrees of freedom. # must be greater than 2.

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: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options.

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

maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance, 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 mgarch 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. 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). This option may not be specified with from().

The following option is available with mgarch 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 . mgarch dvech ( D.tbill = LD.tbill), arch(1)

Same as above, but constraining the lagged effect of on D.tbill to be zero and suppressing the constants . mgarch 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 . mgarch 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 . mgarch dvech (acme anvil = , noconstant), arch(1/2) garch(1)


Stored results

mgarch dvech stores the following in e():

Scalars e(N) number of observations e(k) number of parameters e(k_extra) number of extra estimates added to _b 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 e(p) significance e(estdf) 1 if distribution parameter was estimated, 0 otherwise e(usr) user-provided distribution parameter e(tmin) minimum time in sample e(tmax) maximum time in sample e(N_gaps) number of gaps e(rank) rank of e(V) e(ic) number of iterations e(rc) return code e(converged) 1 if converged, 0 otherwise

Macros e(cmd) mgarch e(model) dvech e(cmdline) command as typed e(depvar) 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) time variable 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(dist) distribution for error term: gaussian or t e(arch) specified ARCH terms e(garch) specified GARCH terms e(svtechnique) maximization technique(s) for starting values e(technique) maximization technique 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 e(marginsdefault) default predict() specification for margins e(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

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(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance e(pinfo) parameter information, used by predict

Functions e(sample) marks estimation sample

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