New estimation command sspace fits linear state-space models by
maximum likelihood. In state-space models, the dependent variables are
linear functions of unobserved states and observed exogenous variables.
This includes VARMA, structural time-series, some linear dynamic, and
some stochastic general-equilibrium models. sspace can estimate
stationary and nonstationary models.
New estimation command dvech estimates diagonal vech multivariate
GARCH models. These models allow the conditional variance matrix of the
dependent variables to follow a flexible dynamic structure in which each
element of the current conditional variance matrix depends on its own
past and on past shocks.
New estimation command dfactor estimates dynamic-factor models.
These models allow the dependent variables and the unobserved factor
variables to have vector autoregressive (VAR) structures and to
be linear functions of exogenous variables.
allow Stata’s new factor-variable varlist
notation. Also, these estimation commands allow the standard set of
factor-variable–related reporting options.
New postestimation command
which calculates marginal means, predictive margins, marginal effects,
and average marginal effects, is available after
all time-series estimation commands, except svar.
Click here for more information.
New display option vsquish for estimation commands, which allows
you to control the spacing in output containing time-series operators or
factor variables, is available after all time-series estimation
New display option coeflegend for estimation commands, which
displays the coefficients' legend showing how to specify them in an
expression, is available after all time-series estimation commands.
predict after regress now allows time-series operators in option
dfbeta(); see [R] regress postestimation. Also allowing
time-series operators are regress postestimation commands
estat szroeter, estat hettest, avplot, and avplots.
Existing estimation commands mlogit, ologit, and
oprobit now allow time-series operators.
Existing estimation commands
arima now accept
maximization option showtolerance.
Existing estimation command arch now allows you to fit models
assuming that the disturbances follow Student’s t
distribution or the generalized error distribution, as well as the
Gaussian (normal) distribution. Specify which distribution to use with
option distribution(). You can specify the shape or
degree-of-freedom parameter, or you can let arch estimate it along
with the other parameters of the model.