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 newey, prais, sspace, dvech, and dfactor 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 margins, which calculates marginal means, predictive margins, marginal effects, and average marginal effects, is available after all time-series estimation commands, except svar.
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 commands.
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 arch and 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.