Stata's expertise lies in the analysis of time based data. Stata provides not only the basic time series models like ARIMA but even the multivariate equivalents (VAR/VECModels) as well. Further you are able to model volatility using GARCHmodels in Stata. KaplanMeiercurves are the way to analyse survival times, while mixed models help to analyse panel data. A mighty scripting language completes the package.
Stata produces all kinds of classical statistics. You can use it for descriptive statistics, hypothesis testing and visualization of data. Typically Stata is used in research and development. The large amount of different statistical methods helps scientists in all fields of applications (Social science, econometrics, epidimiology, medical research).
No matter if you are a student or a senior researcher, there is always the right version of STATA available: Stata/IC, Stat/ SE and Stata/MP
Arguments for Stata:
 Used in research and development
 Wide range of statistical and graphical methods
 Comprehensive statistical software
 Flexible and especially powerful for analysis of time series
 Easy to learn but mighty scripting language
Recommended products
NLOGIT 6 (includes Limdep 11)
STATA 15/MP
Stata 15/SE
Trialversion of Stata
The producer provides a free 30day trialversion on their website. The trialversion contains all the features of Stata. You can register for this license simply by visiting the following link: http://www.stata.com/customerservice/evaluatestata/
Windows  Mac  Linux  
Further Requirements  Stata for Unix requires a video card that can display thousands of colors or more (16bit or 24bit color)  
Operating System  Windows XP, Vista, 7, 8, 10, Windows Server 2003, 2008, 2012 (32/64Bit)  Mac OS X 10.7 or higher (64Bit)  Any 64bit (x8664 or compatible) or 32bit (x86 or compatible) running Linux 
Min. CPU  
Min. RAM  512 MB  
Disk Space  900 MB 
Latent class analysis (LCA)Discover and understand the unobserved groupings in your data. Use LCA's modelbased classification to find out

bayes: logistic ...

Markdown & dynamic documents

Linearized DSGEsWrite your model in simple algebraic form. Stata does the rest: solve model, estimate parameters, estimate policy and transition matrices (with CIs), estimate and graph IRFs, and perform forecasts. 
Finite mixture models (FMMs)

Spatial autoregressive modelsBecause sometimes where you are matters. 
Intervalcensored survival modelsFit any of Stata's six parametric survival models to intervalcensored data. All the usual survival features are supported: stratified estimation, robust and clustered SEs, survey data, graphs, and more. 
Nonlinear multilevel

Mixed logit models: Advanced choice modelingDo you walk to work, ride a bus, or drive your car? Which of three insurance plans do you buy? Which political party do you vote for? We make dozens of choices every day. Researchers have access to gaggles of data about those choices. Mixed logit introduces random effects into choice modeling and thereby relaxes the IIA assumption and increases model flexibility. 
Nonparametric regressionWhen you know something matters. But have no idea how. 
Create Word documents from Stata

Bayesian multilevel modelsSmall number of groups? Consider Bayesian multilevel modeling. 
Threshold regressionYour timeseries regression may change parameters at some point in time or at multiple points in time. The activity of foraging animals might follow a completely different pattern at temperatures above some threshold. You may not know the value of that threshold. Finding such thresholds and estimating the parameters within the regimes is what threshold regression does. 
Paneldata tobit with random coefficientsStata has long had estimators for random effects (random intercepts) in panel data. 
Search, browse, and import FRED dataThe St. Louis Federal Reserve makes available over 470,000 U.S. and international economic and financial time series. You can now easily search, browse, and import these data. 
Multilevel regression for intervalmeasured outcomesIncomes are sometimes recorded in groupings, as are people's weights, insect counts, gradepoint averages, and hundreds of other measures. Often we have repeated measurements for individuals, or schools, or orchards, etc. So ... we need multilevel regression for intervalmeasured (intervalcensored) outcomes. 
Multilevel tobit regression for censored outcomes

Paneldata cointegration tests

Tests for multiple breaks in time series

Multiplegroup generalized SEMGeneralized SEM now supports multiplegroup analysis. Easily specify groups and test parameter invariance across groups. GSEM models include

ICD10CM/PCS

Power for cluster randomized designsPower analysis for comparing
when you randomize clusters instead of individuals 
Power for linear regression models

Heteroskedastic linear regression

Poisson models with sample selectionCounts are common. How many: Fish did you catch?
Accidents occurred? Patents does a firm generate? Outcomes are not always seen. Folks evade the game warden.
Accidents are not always reported. Some firms prefer trade secrets to patents. So you need Poisson models with sample selection. 
More in panel dataNonlinear models with random effects, including random coefficients Bayesian paneldata models Interval regression with random intercepts and random coefficients 
More in graphicsTransparency in graphs SVG export 
More in statisticsBayesian survival models Zeroinflated ordered probit Add your own power and samplesize methods Bayesian sampleselection models And yet more 
More in the interfaceStata in Swedish Stata in Chinese Improvements to the Dofile Editor 
And, even more
Stream randomnumber generator Improvements for Java plugins
The whole feature list you will find under the following link:
https://www.stata.com/features/
Stata Features
Data management
data transformations, matchmerge, ODBC, XML, bygroup processing, append files, sort, row–column transposition, labeling, saving results
Basic statistics
summaries, crosstabulations, correlations, t tests, equalityofvariance tests, tests of proportions, confidence intervals, factor variables
Linear models
regression; bootstrap, jackknife, and robust Huber/White/sandwich variance estimates; instrumental variables; threestage least squares; constraints; quantile regression; GLS
Multilevel mixedeffects models
generalized linear models;continuous, binary, and count outcomes; two, three, and higherlevel models; randomintercepts; randomslopes; crossed random effects; BLUPs of effects and fitted values; hierarchical models; residual error structures; support for survey data in linear models
Binary, count, and discrete outcomes
logistic, probit, tobit; Poisson and negative binomial; conditional, multinomial, nested, ordered, rankordered, and stereotype logistic; multinomial probit; zeroinflated and lefttruncated count models; selection models; marginal effects
Longitudinal data/panel data
random and fixed effects with robust standard errors; linear mixed models, randomeffects probit, GEE, random and fixedeffects Poisson, dynamic paneldata models, and instrumentalvariables regression; panel unitroot tests; AR(1) disturbances
Generalized linear models (GLMs)
ten link functions, userdefined links, seven distributions, ML and IRLS estimation, nine variance estimators, seven residuals
Nonparametric methods
WilcoxonMannWhitney, Wilcoxon signed ranks and KruskalWallis tests; Spearman and Kendall correlations; KolmogorovSmirnov tests; exact binomial CIs; survival data; ROC analysis; smoothing; bootstrapping
Exact statistics
exact logistic and Poisson regression, exact casecontrol statistics, binomial tests, Fisher's exact test for r × c tables
ANOVA/MANOVA
balanced and unbalanced designs; factorial, nested, and mixed designs; repeated measures; marginal means; contrasts
Multivariate methods
factor analysis, principal components, discriminant analysis, rotation, multidimensional scaling, Procrustean analysis, correspondence analysis, biplots, dendrograms, userextensible analyses
Cluster analysis
hierarchical clustering; kmeans and kmedian nonhierarchical clustering; dendrograms; stopping rules; userextensible analyses
Resampling and simulation methods
bootstrapping, jackknife and Monte Carlo simulation; permutation tests
Tests, predictions, and effects
Wald tests; LR tests; linear and nonlinear combinations, predictions and generalized predictions, marginal means, leastsquares means, adjusted means; marginal and partial effects; forecast models; Hausman tests
Graphics
line charts, scatterplots, bar charts, pie charts, hilo charts, regression diagnostic graphs, survival plots, nonparametric smoothers, distribution QQ plots
Survey methods
multistage designs; bootstrap, BRR, jackknife, linearized, and SDR variance estimation; poststratification; DEFF; predictive margins; means, proportions, ratios, totals; summary tables; regression, instrumental variables, probit, Cox regression
Survival analysis
KaplanMeier and NelsonAalen estimators,; Cox regression (frailty); parametric models (frailty); competing risks; hazards; timevarying covariates; left and rightcensoring, Weibull, exponential, and Gompertz analysis
Epidemiology
standardization of rates, case–control, cohort, matched casecontrol, MantelHaenszel, pharmacokinetics, ROC analysis, ICD9CM
Time series
ARIMA; ARFIMA; ARCH/GARCH; VAR; VECM; multivariate GARCH; unobserved components model; dynamic factors; statespace models; business calendars; correlograms; periodograms; forecasts; impulseresponse functions; unitroot tests; filters and smoothers; rolling and recursive estimation
Multiple imputation
nine univariate imputation methods; multivariate normal imputation; chained equations; explore pattern of missingness; manage imputed datasets; fit model and pool results; transform parameters; joint tests of parameter estimates; predictions
Simple maximum likelihood
specify likelihood using simple expressions; no programming required; survey data; standard, robust, bootstrap, and jackknife SEs; matrix estimators
Programmable maximum likelihood
userspecified functions; NR, DFP, BFGS, BHHH; OIM, OPG, robust, bootstrap, and jackknife SEs; Wald tests; survey data; numeric or analytic derivatives
Other statistical methods
kappa measure of interrater agreement; Cronbach's alpha; stepwise regression; tests of normality
Programming features
adding new commands; command scripting; objectoriented programming; menu and dialogbox programming; Project Manager; plugins
Matrix programmingMata
interactive sessions, largescale development projects, optimization, matrix inversions, decompositions, eigenvalues and eigenvectors, LAPACK engine, real and complex numbers, string matrices, interface to Stata datasets and matrices, numerical derivatives, objectoriented programming
Internet capabilities
ability to install new commands, web updating, web file sharing, latest Stata news
Accessibility
Section 508 compliance, accessibility for persons with disabilities
Sample session
A sample session of Stata for Mac, Unix, or Windows.
Userwritten commands
Userwritten commands for metaanalysis, data management, survival, econometrics
Graphical user interface
menus and dialogs for all features; Data Editor; Variables Manager; Graph Editor; Project Manager; Dofile Editor; Clipboard Preview Tool; multiple preference sets
Graphics
line charts; scatterplots; bar charts; pie charts; hilo charts; contour plots; GUI Editor; regression diagnostic graphs; survival plots; nonparametric smoothers; distribution QQ plots
Documentation
20 manuals20 manuals; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references; 11,000+ pages; seamless navigation; thousands of worked examples; methods and formulas; references
Power and sample size
power; sample size; effect size; minimum detectable effect; means; proportions; variances; correlations; casecontrol studies; cohort studies; survival analysis; balanced or unbalanced designs; results in tables or graphs
Treatment effects
inverse probability weight (IPW); doubly robust methods; propensity score matching; regression adjustment; covariate matching; multilevel treatments; average treatment effects (ATEs); average treatment effects on the treated (ATETs); potentialoutcome means (POMs)
SEM (Structural equation modeling)
graphical path diagram builder; standardized and unstandardized estimates; modification indices; direct and indirect effects; continuous, binary, count, and ordinal outcomes (GLM); multilevel models; random slopes and intercepts; factors scores, empirical Bayes, and other predictions; groups and tests of invariance; goodness of fit; handles MAR data by FIML; correlated data
Functions
statistical; randomnumber; mathematical; string; date and time
Embedded statistical computations
Numerics by Stata
Contrasts, pairwise comparisons, and margins
compare means, intercepts, or slopes; compare to reference category, adjacent category, grand mean, etc.; orthogonal polynomials; multiple comparison adjustments; graph estimated means and contrasts; interaction plots
GMM an nonlinear regression
generalized method of moments (GMM); nonlinear regression