This page contains only historical information and is not about the current
release of Stata.
Please see our Stata 14 page
for information on the current version of Stata.
What’s new in time series
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.
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
- 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
- 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
- Estimation commands now respect the width of the Results window.
This feature may be turned off by new display option
- 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
- 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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