What’s new in time-series analysis
- 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.
- Estimation commands
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.
- Existing command tsappend is now faster.
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