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 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. 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 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.
• Existing command tsappend is now faster.

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