This page contains only historical information and is not about the current release of Stata. Please see our Stata 16 page for information on the current version of Stata.

What’s new in time series

• MGARCH, which is to say, multivariate GARCH, which is to say, estimation of multivariate generalized autoregressive conditional heteroskedasticity models of volatility, and this includes constant, dynamic, and varying conditional correlations, also known as the CCC, DCC, and VCC models. Innovations in these models may follow multivariate normal or Student’s t distributions.
• UCM, which is to say, unobserved-components models, also known as structural time-series models that decompose a series into trend, seasonal, and cyclical components, and which were popularized by Harvey (1989).
• ARFIMA, which is to say, autoregressive fractionally integrated moving-average models, useful for long-memory processes.
• Filters for extracting business and seasonal cycles. Four popular time-series filters are provided: the Baxter–King and Christiano–Fitzgerald band-pass filters, and the Butterworth and the Hodrick–Prescott high-pass filters.
• Business calendars allow you to define your own calendars so that they display correctly and lags and leads work as they should. You could create file lse.stbcal that recorded the days the London Stock Exchange is open (or closed) and then Stata would understand format %tblse just as it understands the usual date format %td. Once you define a calendar, Stata deeply understands it. You can, for instance, easily convert between %tblse and %td values.
• Improved documentation for date and time variables.
• Contrasts, which is to say, tests of linear hypotheses involving factor variables and their interactions from the most recently fit model. Tests include ANOVA-style tests of main effects, simple effects, interactions, and nested effects. Effects can be decomposed into comparisons with reference categories, comparisons of adjacent levels, comparisons with the grand mean, and more. New commands contrast and margins, contrast are available after many time-series estimation commands.
• Pairwise comparisons available after many time-series estimation commands.
• Graphs of margins, marginal effects, contrasts, and pairwise comparisons available after most time-series estimation commands.
• Estimation output improved.
  
  
  • Implied zero coefficients now shown. When a coefficient is omitted, it is now shown as being zero and the reason it was omitted—collinearity, base, empty—is shown in the standard-error column. (The word “omitted” is shown if the coefficient was omitted because of collinearity.)
  • You can set displayed precision for all values in coefficient tables using set cformat, set pformat, and set sformat. Or you may use options cformat(), pformat(), and sformat() on all estimation commands.
  • Estimation commands now respect the width of the Results window. This feature may be turned off by new display option nolstretch.
  • You can now set whether base levels, empty cells, and omitted are shown using set showbaselevels, set showemptycells, and set showomitted.
• Spectral densities from parametric models via new postestimation command psdensity lets you estimate using arfima, arima, and ucm and then obtain the implied spectral density.
• dvech renamed mgarch dvech. The command for fitting the diagonal VECH model is now named mgarch dvech, and innovations may follow multivariate normal or Student’s t distributions.
• Loading data from Haver Analytics supported on all 64-bit Windows.
• Option addplot() now places added graphs above or below. Graph commands that allow option addplot() can now place the added plots above or below the command’s plots. Affected by this are the commands corrgram, cumsp, pergram, varstable, vecstable, wntestb, and xcorr.

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