This page contains only historical information and is not about the current
release of Stata.
Please see our Stata 10 page
for information on the current version of Stata.
More statistics
In addition to mixed models, survey statistics, multivariate
statistics, and multinomial probit, many other new estimators
and a host of statistical features have been added in Stata 9.
Categories
Also see the separate sections on
multinomial mixed models,
survey statistics,
multivariate statistics, and
multinomial probit
- Existing command arima can now estimate multiplicative seasonal
ARIMA (SARIMA) models; see new options sarima(), mar(), and
mma() in [TS] arima.
- New command rolling performs rolling-window or recursive estimations,
including regressions, and collects statistics from the estimation on each
window; see [TS] rolling.
- The [TS] manual now has a glossary that defines commonly used terms
in time-series analysis and explains how we use them in the manual; see
the glossary of [TS].
- Many existing commands that previously did not allow time-series
operators now do. These commands include areg, binreg, biprobit,
boxcox, cloglog, cnsreg, glm, heckman,
heckprob, hetprob, impute, intreg,
logistic, logit, lowess, mvreg, nbreg,
orthog, pcorr, poisson, probit, pwcorr,
rreg, testparm, treatreg, truncreg,
xtcloglog, xtgls, xtintreg, xtlogit,
xtpoisson, xtprobit, xtgee, xtreg,
xtsum, and xttobit.
- Many commands requiring time-series data now work on a single panel
from a panel dataset when that panel is selected using an if
expression or an in qualifier. Those commands include ac,
corrgram, cumsp, dfgls, dfuller, pac,
pergram, pperron, wntestb, wntestq, and
xcorr. New commands estat archlm, estat bgodfrey,
estat dwatson, and estat durbinalt, which replace commands
archlm, bgodfrey, dwstat, and durbina,
also work on a single panel from a panel dataset.
- The dialogs for analyzing IRF results are much improved. The dialogs
now populate lists of models and variables from the current IRF results
that may be chosen for producing tables and graphs. The improved dialogs
include irf cgraph, irf ctable, irf graph,
irf ograph, and irf table.
- Existing command dfuller has new option drift for testing
the null hypothesis of a random walk with drift. The algorithm for
calculating MacKinnon’s approximate p-values is also now more accurate in
cases where the p-value is relatively large; see
[TS] dfuller.
- Existing commands corrgram and pac have new option yw that
computes partial autocorrelations using the Yule–Walker equations instead
of the default regression-based method; see
[TS] corrgram.
- Time-series operators are now better displayed in estimation and other
result tables.
- New command estat durbinalt—used after regress—brings together what
was previously done by commands dwstat, durbina, bgodfrey, and archlm.
The new commands are estat dwatson, estat durbina, estat bgodfrey,
and estat archlm. See
[R] regress postestimation time series.
- The ability of arima and arch to estimate standard errors using
either the observed information matrix (OIM) or the outer product of
gradients (OPG) has been consolidated under the new vce() option.
(What follows was first released in Stata 8.2.)
- New command vec fits cointegrated vector error-correction models
(VECMs) using Johansen’s method; see
[TS] vec.
- New command vecrank produces statistics used to determine the number
of cointegrating vectors in a VECM, including Johansen’s trace and
maximum-eigenvalue tests for cointegration; see
[TS] vecrank.
- New command fcast—which replaces old command
varfcast—produces and graphs dynamic forecasts of the
dependent variables after fitting a VAR, SVAR, or VECM; see [TS]
fcast.
- New command irf—which replaces the old command
varirf—does everything the old command did and more.
irf estimates the impulse–response functions, cumulative
impulse–response functions, orthogonalized impulse–response
functions, structural impulse–response functions, and forecast
error-variance decompositions after fitting a VAR, SVAR, or VECM.
irf can also make graphs and tables of the results. See [TS]
irf.
varirf continues to work but is no longer documented. irf accepts
.vrf result files created by varirf.
- Existing command varsoc can now be used to obtain lag-order selection
statistics for VECMs, as well as VARs; see
[TS] varsoc.
- New command veclmar computes Lagrange-multiplier statistics for
autocorrelation after fitting a VECM; see
[TS] veclmar.
- New command vecnorm tests whether the disturbances in a VECM are
normally distributed. For each equation and for all equations jointly,
three statistics are computed: a skewness statistic, a kurtosis statistic,
and the Jarque–Bera statistic. See
[TS] vecnorm.
- New command vecstable checks the eigenvalue stability condition after
fitting a VECM; see
[TS] vecstable.
- New command vecstable and the existing command varstable have
a new graph option for presenting the stability results. See
[TS] vecstable
and [TS] varstable.
- The output of the following commands has been standardized to improve
formatting: var, svar, vargranger, varlmar,
varnorm, varsoc, varstable, and varwle.
- New command haver makes it easy to load and analyze economic and
financial databases available from Haver Analytics; see
[TS] haver.
- The big news is the new commands xtmixed—Stata now fits linear
mixed models. See the section on
linear mixed models.
- New features have been added to the maximum likelihood estimators that do
not have closed-form solutions and require numeric evaluation of the
likelihood. These estimators include
xtlogit,
xtprobit,
xtpoisson,
xtcloglog,
xtintreg, and
xttobit.
- The likelihood may now be approximated using adaptive
Gauss–Hermite quadrature (the new default) or nonadaptive quadrature
(the previous default). Adaptive quadrature substantially increases the
accuracy of the approximation, particularly on difficult problems such as
data with large panel sizes or data with a large variance for the random
effects.
- Linear constraints may now be imposed using the new option constraints().
Constraints are specified the standard way; see
[R] constraint.
- New option intpoints() replaces old option quad(),
although quad() continues to work. The new name is more
meaningful, especially when used with estimators that integrate
likelihoods using methods other than quadrature.
- Existing command xtreg now allows options robust and cluster()
when estimating fixed-effects (FE) and random-effects (RE) models; see
[XT] xtreg.
- Most [XT] commands that previously did not allow time-series operators
now support them. These commands include
xtgls,
xtreg,
xtsum,
xtcloglog,
xtintreg,
xtlogit,
xtpoisson,
xtprobit,
xttobit, and
xtgee.
- New command xtrc is old command xtrchh, renamed, and with
new features. New option beta reports the best linear predictors
(BLUPs) for the group-specific coefficients, along with their standard
errors and confidence intervals. For details, see [XT] xtrc.
- predict after xtrc has the new option group() to
compute the BLUPs of the dependent variable using the BLUPs of the
coefficients.
- New command xtline plots panel data and allows either overlaid or
separate graphs for each panel; see [XT] xtline.
- New section [XT] glossary defines commonly used terms and how they
are used by us.
- The [ST] manual now has a glossary that defines commonly used terms
in survival (or duration) analysis and often explains how these terms are
used in the manual; see the glossary of [ST].
- New command estat can be used after stcox and streg.
In addition to the standard estat statistics—information
criteria, estimation sample summary, and formatted variance–covariance
matrix (VCE)—statistics specific to the proportional-hazards
estimator are available after stcox. These include
- estat concordance computes Harrell’s C and Somers' D
statistics measuring concordance—agreement of predictions
with observed failure order.
- estat phtest replaces the existing stphtest for
computing tests and graphs of the proportional hazards assumption.
stphtest continues to work.
See [ST] stcox postestimation and [ST] streg postestimation.
- Existing command sts graph has new options cihazard and
per(#). cihazard draws pointwise confidence bands
around the smoothed hazard function, and per() specifies the
units used to report the survival or failure rate. See [ST] sts.
- Existing command stcurve now plots over an evenly spaced grid,
producing smooth curves, even in small samples; see [ST] stcurve.
- Existing command sts graph has new options atriskopts() and
lostopts() that let you control how the labels for at-risk and lost
observations look (their color, font size, etc.); see [ST] sts.
- Existing command stci has new options for controlling how the
plotted survival line looks (color, thickness, etc.) and for adding
titles, controlling legends, and all other characteristics of the graph;
see [ST] stci.
Command ml, for implementing user-written maximum likelihood estimators,
has many new features:
- New option technique() sets the optimization technique. BHHH,
DFP, and BFGS optimization techniques are now available; the default
technique remains modified Newton–Raphson.
- New option vce() sets the type of covariance matrix calculations
that will be made.
vce(oim) specifies the observed information matrix (OIM), also
called the Hessian-based estimator; this is (and always has been) the
default.
vce(opg) specifies the outer product of the gradients (OPG).
This is new.
vce(robust) specifies Taylor-series linearization, also known
as the Huber or White estimator and, in Stata, as simply robust.
- Most estimators written with ml now support estimation with
survey data and correlated data with no additional programming. This
support includes correct treatment of multistage designs, weighting,
stratification, poststratification, and finite-population corrections,
as well as access to linearization, jackknife, and bootstrap
variance estimators. For a discussion, see [P] program properties.
- ml has always allowed linear constraints to be applied using the
option constraints() with no additional programming. It now
handles irrelevant constraints more elegantly. Irrelevant constraints
are those that have no impact on the model. Previously, irrelevant
constraints caused an error message. Now they are flagged and
ignored.
- When linear constraints are imposed, ml now applies a Wald test
for the overall fit of the model, rather than attempting a
likelihood-ratio (LR) test, which is often inappropriate.
- ml has new subcommand score for generating scores after
fitting a model.
- ml has new option diparm_options() that automatically
performs transformations of ancillary parameters.
- ml now saves the gradient vector in e(gradient).
- ml has new option search(norescale) that prevents
rescaling when searching for starting values.
- ml honors the new setting for maximum iterations, set maxiter
#, and will iterate a maximum of # iterations, even if
convergence has not been achieved.
- ml now displays a prominent message in the footer of the
estimation results when convergence is not achieved. This message
continues to be shown on redisplay of estimation results.
- ml has new option nofootnote to suppress printing
the new message warning if convergence is not achieved.
- ml tests for convergence using the Hessian-scaled
gradient—g*inv(H)*g'. This is a true convergence criterion that
ensures that the gradient is close to zero when scaled by the Hessian (the
curvature of the likelihood or pseudolikelihood surface at the optimum).
This new criterion is particularly important when maximizing difficult
likelihoods to prevent stopping the maximization too soon.
- New option nrtolerance() lets you change the tolerance for the
Hessian-scaled gradient convergence criterion; the default is
nrtolerance(1e-5).
- New option shownrtolerance displays the criterion value of the
Hessian-scaled gradient at each iteration.
- New undocumented command mlmatbysum helps you compute the
Hessian of panel-data likelihoods and is of interest to those seeking
the speed that comes with programming your own second-derivative
calculations; see mlmatbysum.
- ml has two new undocumented subcommands—ml hold and
ml unhold—to assist in solving nested optimization
problems; see
ml_hold.
See
[R] ml for more information on these features. Anyone
programming estimators using ml should read the book
Maximum Likelihood
Estimation with Stata, 2nd Edition (Gould, Pitblado, and Sribney
2003). Many of the features mentioned above are discussed and applied to
real problems in the book.
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