LINEAR MODELS

regression  •  censored outcomes  •  endogenous regressors  •  bootstrap, jackknife, and robust and cluster–robust variance  •  wild cluster bootstrap  •  instrumental variables  •  three-stage least squares  •  constraints  •  quantile regression  •  GLS  •  DID  •

… more

PANEL/LONGITUDINAL DATA

random and fixed effects with robust standard errors  •  linear mixed models  •  random-effects probit  •  GEE  •  random- and fixed-effects Poisson  •  dynamic panel-data models  •  instrumental variables  •  DID  •  panel unit-root tests  •

… more

MULTILEVEL MIXED-EFFECTS MODELS

continuous, binary, count, and survival outcomes  •  two-, three-, and higher-level models  •  generalized linear models  •  nonlinear models  •  random intercepts  •  random slopes  •  crossed random effects  •  BLUPs of effects and fitted values  •  hierarchical models  •  residual error structures  •  DDF adjustments  •  support for survey data  •

… more

BINARY, COUNT, AND LIMITED OUTCOMES

logistic, probit, tobit  •  Poisson and negative binomial  •  conditional, multinomial, nested, ordered, rank-ordered, and stereotype logistic  •  multinomial probit  •  zero-inflated and left-truncated models  •  selection models  •  marginal effects  •

… more

CHOICE MODELS

discrete choice  •  rank-ordered alternatives  •  conditional logit  •  multinomial probit  •  nested logit  •  mixed logit  •  panel data  •  case-specific and alternative-specific predictors  •  interpret results—expected probabilities, covariate effects, comparisons across alternatives  •

… more

EXTENDED REGRESSION MODELS (ERMs)

endogenous covariates  •  sample selection  •  nonrandom treatment  •  panel data  •  account for problems alone or in combination  •  continuous, interval-censored, binary, and ordinal outcomes  •

… more

GENERALIZED LINEAR MODELS (GLMs)

ten link functions  •  user-defined links  •  seven distributions  •  ML and IRLS estimation  •  nine variance estimators  •  seven residuals  •

… more

FINITE MIXTURE MODELS (FMMs)

fmm: prefix for 17 estimators  •  mixtures of a single estimator  •  mixtures combining multiple estimators or distributions  •  continuous, binary, count, ordinal, categorical, censored, truncated, and survival outcomes  •

… more

SPATIAL AUTOREGRESSIVE MODELS

spatial lags of dependent variable, independent variables, and autoregressive errors  •  fixed and random effects in panel data  •  endogenous covariates  •  analyze spillover effects  •

… more

ANOVA/MANOVA

balanced and unbalanced designs  •  factorial, nested, and mixed designs  •  repeated measures  •  marginal means  •  contrasts  •

… more

EXACT STATISTICS

exact logistic and Poisson regression  •  exact case–control statistics  •  binomial tests  •  Fisher’s exact test for r × c tables  •

… more

EPIDEMIOLOGY

standardization of rates  •  case–control  •  cohort  •  matched case–control  •  Mantel–Haenszel  •  pharmacokinetics  •  ROC analysis  •  ICD-10  •  additive models of risk  •

… more

DSGE MODELS

specify models algebraically  •  solve models  •  estimate parameters  •  identification diagnostics  •  policy and transition matrices  •  IRFs  •  dynamic forecasts  •  Bayesian  •

… more

TESTS, PREDICTIONS, AND EFFECTS

Wald tests  •  LR tests  •  linear and nonlinear combinations  •  predictions and generalized predictions  •  marginal means  •  least-squares means  •  adjusted means  •  marginal and partial effects  •  forecast models  •  Hausman tests  •

… more

CONTRASTS, PAIRWISE COMPARISONS, AND MARGINS

compare means, intercepts, or slopes  •  compare with reference category, adjacent category, grand mean, etc.  •  orthogonal polynomials  •  multiple-comparison adjustments  •  graph estimated means and contrasts  •  interaction plots  •

… more

RESAMPLING AND SIMULATION METHODS

bootstrap  •  jackknife  •  Monte Carlo simulation  •  permutation tests  •  exact p-values  •

… more

MULTIVARIATE METHODS

factor analysis  •  principal components  •  discriminant analysis  •  rotation  •  multidimensional scaling  •  Procrustean analysis  •  correspondence analysis  •  biplots  •  dendrograms  •  user-extensible analyses  •

… more

CLUSTER ANALYSIS

hierarchical clustering  •  kmeans and kmedian nonhierarchical clustering  •  dendrograms  •  stopping rules  •  user-extensible analyses  •

… more

NETWORK ANALYSIS

nwcommands: import and manipulate networks  •  generate networks  •  calculate centrality and dissimilarity measures  •  visualize networks  •

… more

TIME SERIES

ARIMA  •  ARFIMA  •  ARCH/GARCH  •  VAR  •  VECM  •  multivariate GARCH  •  unobserved-components model  •  dynamic factors  •  state-space models  •  Markov-switching models  •  business calendars  •  tests for structural breaks  •  threshold regression  •  forecasts  •  impulse–response functions  •  local projections  •  unit-root tests  •  filters and smoothers  •  rolling and recursive estimation  •  Bayesian  •

… more

SURVIVAL ANALYSIS

Kaplan–Meier and Nelson–Aalen estimators  •  Cox regression (frailty)  •  parametric models (frailty, random effects)  •  competing risks  •  hazards  •  time-varying covariates  •  left-, right-, and interval-censoring  •  Weibull, exponential, and Gompertz models  •

… more

BAYESIAN ANALYSIS

thousands of built-in models  •  univariate and multivariate models  •  linear and nonlinear models  •  panel data  •  multilevel models  •  VAR  •  DSGE  •  continuous, binary, ordinal, and count outcomes  •  bayes: prefix for 58 estimation commands  •  continuous univariate, multivariate, and discrete priors  •  add your own models  •  multiple chains  •  convergence diagnostics  •  posterior summaries  •  hypothesis testing  •  model fit  •  model comparison  •  predictions  •  dynamic forecast  •  impulse-response functions  •

… more

BAYESIAN MODEL AVERAGING

full enumeration  •  MC3 and MH sampling  •  three model prior classes  •  fixed and random g-priors for coefficients  •  heredity rules  •  PIP for predictors  •  model ranking by PMP  •  BMA convergence  •  variable-inclusion maps  •  model-size distribution plots  •  jointness measures  •  log predictive-score  •  predictions  •

… more

META-ANALYSIS

effect sizes  •  common, fixed, and random effects  •  forest, funnel, and more plots  •  subgroup, leave-one-out, and cumulative analysis  •  meta-regression  •  small-study effects  •  publication bias  •  multivariate  •  multilevel  •

… more

POWER, PRECISION, AND SAMPLE SIZE

power  •  sample size  •  effect size  •  minimum detectable effect  •  CI width  •  means  •  proportions  •  variances  •  correlations  •  ANOVA  •  regression  •  cluster randomized designs  •  case–control studies  •  cohort studies  •  contingency tables  •  survival analysis  •  balanced or unbalanced designs  •  results in tables or graphs  •  group sequential designs for clinical trials  •

… more

inverse probability weight (IPW)  •  doubly robust methods  •  propensity-score matching  •  regression adjustment  •  covariate matching  •  DID  •  multilevel treatments  •  endogenous treatments  •  average treatment effects (ATEs)  •  ATEs on the treated (ATETs)  •  potential-outcome means (POMs)  •  continuous, binary, count, fractional, and survival outcomes  •  panel data  •  lasso  •  casual mediation analysis  •

…more

LASSO

lasso  •  elastic net  •  model selection  •  prediction  •  inference  •  continuous, binary, count, and survival outcomes  •  cross-validation  •  adaptive lasso  •  double selection  •  partialing out  •  cross-fit partialing out  •  double machine learning  •  endogenous covariates  •  treatment effects  •

… more

SEM (STRUCTURAL EQUATION MODELING)

graphical path diagram builder  •  standardized and unstandardized estimates  •  modification indices  •  direct and indirect effects  •  continuous, binary, count, ordinal, and survival outcomes  •  multilevel models  •  random slopes and intercepts  •  factor scores, empirical Bayes, and other predictions  •  groups and tests of invariance  •  goodness of fit  •  handles MAR data by FIML  •  correlated data  •  survey data  •

… more

LATENT CLASS ANALYSIS

binary, ordinal, continuous, count, categorical, fractional, and survival items  •  add covariates to model class membership  •  combine with SEM path models  •  expected class proportions  •  goodness of fit  •  predictions of class membership  •

… more

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  •

… more

SURVEY METHODS

multistage designs  •  bootstrap, BRR, jackknife, linearized, and SDR variance estimation  •  poststratification  •  raking  •  calibration  •  DEFF  •  predictive margins  •  means, proportions, ratios, totals  •  summary tables  •  almost all estimators supported  •

… more

IRT (ITEM RESPONSE THEORY)

binary (1PL, 2PL, 3PL), ordinal, and categorical response models  •  item characteristic curves  •  test characteristic curves  •  item information functions  •  test information functions  •  multiple-group models  •  differential item functioning (DIF)  •

… more

DATA MANIPULATION

data transformations  •  data frames  •  match-merge  •  import/export data  •  JDBC  •  ODBC  •  SQL  •  Unicode  •  by-group processing  •  append files  •  sort  •  row–column transposition  •  labeling  •  save results  •

… more

REPORTING

reproducible reports  •  customizable tables  •  graphical tables builder  •  Word  •  Excel  •  PDF  •  HTML  •  dynamic documents  •  Markdown  •  Stata results and graphs  •  SVG  •  EPS  •  PNG  •  TIF  •

… more

GRAPHICS

lines  •  bars  •  areas  •  ranges  •  contours  •  confidence intervals  •  interaction plots  •  survival plots  •  publication quality  •  customize anything  •  Graph Editor  •

… more

PROGRAMMING FEATURES

adding new commands  •  scripting  •  object-oriented programming  •  menu and dialog-box programming  •  dynamic documents  •  Markdown  •  Project Manager  •  Python integration  •  PyStata  •  Jupyter notebook  •  Java integration  •  Java plugins  •  H2O access  •  C/C++ plugins  •

… more

MATA—STATA’S SERIOUS PROGRAMMING LANGUAGE

interactive sessions  •  large-scale development projects  •  optimization  •  matrix inversions  •  decompositions  •  eigenvalues and eigenvectors  •  LAPACK engine  •  Intel^® MKL  •  real and complex numbers  •  string matrices  •  interface to Stata datasets and matrices  •  numerical derivatives  •  object-oriented programming

… more

GRAPHICAL USER INTERFACE

menus and dialogs for all features  •  Data Editor  •  Variables Manager  •  Graph Editor  •  Project Manager  •  Do-file Editor  •  multiple preference sets  •

… more

DOCUMENTATION

35 manuals  •  18,000+ pages  •  seamless navigation  •  thousands of worked examples  •  quick starts  •  methods and formulas  •  references  •

… more

BASIC STATISTICS

summaries  •  cross-tabulations  •  correlations  •  z and t tests  •  equality-of-variance tests  •  tests of proportions  •  confidence intervals  •  factor variables  •

… more

NONPARAMETRIC METHODS

nonparametric regression  •  Wilcoxon–Mann–Whitney, Wilcoxon signed ranks, and Kruskal–Wallis tests  •  Cochran–Armitage and other trend tests  •  Spearman and Kendall correlations  •  Kolmogorov–Smirnov tests  •  exact binomial CIs  •  survival data  •  ROC analysis  •  smoothing  •  bootstrapping  •

… more

NONLINEAR REGRESSION, GMM AND OTHER SYSTEMS OF EQUATIONS

generalized method of moments (GMM)  •  nonlinear regression  •  demand systems  •

… more

SIMPLE MAXIMUM LIKELIHOOD

specify likelihood using simple expressions  •  no programming required  •  survey data  •  standard, robust, bootstrap, and jackknife SEs  •  matrix estimators  •

… more

PROGRAMMABLE MAXIMUM LIKELIHOOD

user-specified functions  •  NR, DFP, BFGS, BHHH  •  OIM, OPG, robust, bootstrap, and jackknife SEs  •  Wald tests  •  survey data  •  numeric or analytic derivatives  •

… more

OTHER STATISTICAL METHODS

kappa measure of interrater agreement  •  Cronbach’s alpha  •  stepwise regression  •  tests of normality  •

… more

FUNCTIONS

statistical  •  random-number  •  mathematical  •  string  •  date and time  •  regular expressions  •  Unicode  •

… more

INTERNET CAPABILITIES

search and download thousands of community-contributed features (see below)  •  web updating  •  web file sharing  •  latest Stata news  •

… more

COMMUNITY-CONTRIBUTED FEATURES

search and download thousands of free additions  •  discover new features in the Stata Journal  •  share commands by posting to the SSC  •  discuss community-contributed features on Statalist  •

… more

EMBEDDED STATISTICAL COMPUTATIONS

Numerics by Stata

… more

INSTALLATION QUALIFICATION

IQ report for regulatory agencies such as the FDA  •  installation verification

… more

FDA COMPLIANCE

Adherence to FDA regulatory requirement for statistical software

… more

ACCESSIBILITY

Section 508 compliance, accessibility for persons with disabilities

… more

1. Bayesian model averaging
   
2. Causal mediation analysis
3. Tables of descriptive statistics
4. Group sequential designs for clinical trials
5. Robust inference for linear models
6. Wild cluster bootstrap
7. Estimation of flexible demand systems
8. TVCs with interval-censored Cox model
9. Goodness-of-fit plots for survival models
10. Lasso for Cox proportional hazards model
11. Heterogeneous difference in differences (DID)
12. Multilevel meta-analysis
13. Meta-analysis for prevalence
14. Local projections for impulse–response functions
15. Model selection for ARIMA and ARFIMA

1. Relative excess risk due to interaction (RERI)
2. Spline function generation
3. Corrected AIC and consistent AIC
4. Instrumental-Variables fractional probit model
5. Instrumental-Variables quantile regression
6. Stata graphs have an all-new style
7. Graph colors by variable
8. Alias variables across frames
9. Saving, using, and describing a set of frames
10. Boost-based regular expressions
11. Vectorized numerical integration
12. New reporting features
13. Do-file Editor enhancements
14. Data Editor enhancements
15. More new features in Stata 18

The new bma suite performs Bayesian model averaging to account for model uncertainty in user’s analysis. If researchers are uncertain as to which predictors to include in their linear regression model? They can use bmaregress to find out which predictors are important. Perform model choice, inference, and prediction. Use many postestimation commands to explore influential models, model complexity, model fit and predictive performance, sensitivity analysis to the assumptions about importance of models and predictors.

….more

CAUSAL MEDIATION ANALYSIS

The new mediate command extends Stata’s powerful causal-inference suite to support causal mediation analysis. Causal analysis identifies and quantifies causal effects. Causal mediation analysis disentangles them. Are these effects mediated through another variable, a mediator?

Choose one of 23 combinations of outcome and mediator models, including linear, logit, and Poisson, to estimate the total effect and decompose it into direct and indirect (through the mediator) effects. Compute controlled direct effects and proportion mediated. Recast effects into odds, risk, and incidence-rate ratios. Plot estimated effects. Obtain predictions.

…more

TABLES OF DESCRIPTIVE STATISTICS

Easily create your Table 1 with the new dtable command. Report descriptive statistics. Compare statistics across groups. Export to Word, Excel, PDF, HTML.

…more

GROUP SEQUENTIAL DESIGNS FOR CLINICAL TRIALS

Designing a clinical trial? In Stata 18, researchers can use the new  gsbounds and gsdesign commands to calculate stopping boundaries for group sequential trials. What sample size is required for each interim analysis? Use gsdesign to find out.

Group sequential designs (GSDs) are a class of adaptive design for clinical trials. In a GSD, the sample size is not fixed in advance; instead, pre-planned interim analyses are conducted to allow early stopping for efficacy or futility. This is done by defining stopping boundaries that control the familywise error rate. gsdesign calculates stopping boundaries and sample sizes for interim analyses with tests of means, proportions, survivor functions, and even user-defined methods.

…more

ROBUST INFERENCE FOR LINEAR MODELS

Stata 18 offers more precise standard errors and confidence intervals (CIs) for three commonly used linear models in Stata: regress, areg, and xtreg, fe.

Small number of clusters? Uneven number of observations per cluster? Use HC2 with degrees-of-freedom adjustment, option vce(hc2 …, dfadjust), or wild cluster bootstrap to obtain valid inference.

Multiple nonnested clusters? Use multiway clustering, option vce(cluster group1 group2 … groupk), to account for potential correlation of observations within different clusters.

…more

WILD CLUSTER BOOTSTRAP

Do your data have a small number of clusters or an uneven number of observations per cluster? Do you want to make inferences about parameters in a linear model? With the new wildbootstrap command, you can now use wild cluster bootstrap (WCB) in these situations.

… more

ESTIMATION OF FLEXIBLE DEMAND SYSTEMS

Eight flexible demands systems in just one convenient command—demandsys! Choose from the Cobb–Douglas demand system, almost ideal demand system (AIDS), generalized AIDS, and more. Estimate the demand for a basket of goods. Compute expenditure and price elasticities to evaluate sensitivity to expenditure and price changes.

…more

TVCS WITH INTERVAL-CENSORED COX MODEL

Stata 17 introduced the stintcox command to fit genuine semiparametric Cox models to interval-censored event-time data. Stata 18 adds support for time-varying covariates (TVCs), commonly used in practice to record characteristics that change during follow-up. So, whether you have a biomarker variable or a policy variable that changes with time, you can now include them to model events of interest known only to lie within some time interval. For instance, the event may be a recurrence of cancer or a change in employment status recorded during one of the follow-ups. Both are examples of interval-censored event-time data, in which the event time is not observed exactly.

Use stintcox to create TVCs automatically by interacting existing covariates with specified deterministic functions of time or specify your own TVCs in the new multiple-record-per-subject data format. Use TVCs to test the proportional-hazards assumption with the Wald test or likelihood-ratio test. And incorporate TVCs in your predictions and plots of survivor and other functions.

…more

GOODNESS-OF-FIT PLOTS FOR SURVIVAL MODELS

Stata 18 provides the new estat gofplot command to produce goodness-of-fit (GOF) plots for survival models. You can use it after four survival models: right-censored Cox (stcox), interval-censored Cox (stintcox), right-censored parametric (streg), and interval-censored parametric (stintreg). Check model fit after stratified models or separately for each by-group.

GOF plots provide visual checks for how well the model fits the data. In survival analysis, these checks are based on so-called Cox–Snell residuals and the assumption that, if a model is correct, these residuals should have a standard exponential distribution. Visually, this assumption is assessed by plotting the residuals against their estimated cumulative hazard—the closer the plotted values are to the 45° line, the better the fit (Cox and Snell 1968).

…more

LASSO FOR COX PROPORTIONAL HAZARDS MODELS

If you have time-to-event data, also known as survival-time or failure-time data, and many predictors, check out the lasso cox and elasticnet cox commands. (And when we say many predictors, we mean hundreds, thousands, or more!) New in Stata 18, these commands expand the existing lasso suite for prediction and model selection to include a high-dimensional semiparametric Cox proportional hazards model.

After lasso cox and elasticnet cox, you can use stcurve to plot the survivor, failure, hazard, or cumulative hazard function or use any of the other postestimation tools available after lasso and elasticnet.

…more

HETEROGENEOUS DIFFERENCE IN DIFFERENCES (DID)

When average treatment effects vary over time and over cohort, you can now use the new hdidregress and xthdidregress commands to estimate heterogeneous average treatment effects on the treated (ATETs). Use hdidregress with repeated cross-sectional data and xthdidregress with panel data. Choose from one of four estimators, including regression adjustment and inverse-probability weighting. Plot ATETs time profiles for each cohort with estat atetplot. Aggregate the ATETs within cohort, time, and exposure to treatment with estat aggregation. Explore more postestimation features.

Treatment effects measure the causal effect of a treatment on an outcome. A treatment is a new drug regimen, a surgical procedure, a training program, or even an ad campaign intended to affect an outcome such as blood pressure, mobility, employment, or sales. It is of interest to estimate an ATET.

The standard difference-in-differences (DID) estimator, implemented in existing commands didregress and xtdidregress, estimates an ATET that is common to all groups across time. When groups are treated at different points in time, the assumption about a constant ATET may be violated. The new commands implement estimation methods that account for heterogeneity of the ATET and provide cohort-specific and time-specific ATET estimates.

…more

MULTILEVEL META-ANALYSIS

You want to analyze results from multiple studies, in which the reported effect sizes are nested within higher-level groupings such as regions or schools. Stata 18 adds two new commands, meta meregress and meta multilevel, to the meta suite to perform multilevel meta-analysis and meta-regression. Include random intercepts and coefficients at different levels of hierarchy, and assume different random-effects covariance structures, including exchangeable and unstructured. Perform sensitivity analysis by placing various constraints on variance components. Assess heterogeneity. Predict random effects and their comparative and diagnostic standard errors. And more.

Multilevel meta-analysis is a powerful statistical tool to synthesize effect sizes with a hierarchical structure, such as in a meta-analysis exploring the impact of a new teaching technique on math testing scores in different school districts. Here the effect sizes are nested within schools that are themselves nested within districts. Multilevel meta-analysis allows us not only to determine the overall effect of the technique but also to assess the variability among the effect sizes at different levels of the hierarchy. This is important because studies within the same district are likely to be similar and thus potentially dependent, and ignoring this dependence may lead to inaccurate results. By properly accounting for the dependence among the effect sizes, we can produce more accurate inference and gain a better understanding of the impact of the teaching technique.

…more

META-ANALYSIS FOR PREVALENCE

The meta suite now supports meta-analysis (MA) of one proportion, or prevalence. Multiple types of effect sizes, confidence intervals, and back-transformations are supported. All standard meta-analysis features such as forest plots and subgroup analysis are supported.

The traditional MA deals with two-sample binary or continuous data where the outcome of interest is measured across two groups typically labeled as the treatment and control groups. For example, an MA may compare the risk of contracting a disease (binary outcome) across two groups: the vaccinated and unvaccinated. Or maybe we want to contrast weight loss (continuous outcome) between two groups of subjects that followed different diets, say, keto versus intermittent fasting.

This two-group setting, however, is not always present in an MA. For example, the United Nations may conduct an MA to evaluate the prevalence of a certain disease across countries to allocate the proper resources to combat it. Or maybe the Department of Education performs an MA to assess the proportion of high school dropouts and uses its results to guide the budget for K–12 education. In both examples, we have one-sample binary data, in which the subjects belong to a single group and the interest lies in the proportion of individuals that experienced a certain event (contracting the disease in the first example and dropping out of high school in the second). In this setting, effect sizes such as Freeman–Tukey-transformed proportions or logit-transformed proportions are typically used in the MA.

…more

LOCAL PROJECTIONS FOR IMPULSE–RESPONSE FUNCTIONS

With impulse–repsonse functions, you can find out how a shock to one variable affects other variables over time. With local projections, you can estimate impulse–response functions directly using multistep regressions. Use the new lpirf command to estimate local projections, and graph or tabulate them with the irf suite.

…more

MODEL SELECTION FOR ARIMA AND ARFIMA

Want to find the best ARIMA or ARFIMA model for your data? Compare potential models using AIC, BIC, and HQIC. Use the new arimasoc and arfimasoc commands to select the best number of autoregressive and moving-average terms.

Researchers using autoregressive moving-average (ARMA) models must decide on the proper number of lags to include for the autoregressive and moving-average parameters in their models. Information criteria, which balance model fit against model parsimony, often guide the choice of the maximum number of lags.

arimasoc and arfimasoc assist in model selection by fitting a collection of autoregressive integrated moving average (ARIMA) or autoregressive fractionally integrated moving average (ARFIMA) models and computing information criteria for each model. arimasoc and arfimasoc compute the Akaike information criterion (AIC), the Bayesian information criterion (BIC), and the Hannan–Quinn information criterion (HQIC). The selected model is the one with the lowest value of the information criterion.

…more

RELATIVE EXCESS RISK DUE TO INTERACTION (RERI)

How do exposures interact to increase risk? Do you suspect that interaction is additive? Use reri to find out. Three measures of two-way interactions are provided: RERI, AP, and SI. Many models that estimate RR are supported, including logistic, binomial generalized linear, and survival.

…more

SPLINE FUNCTION GENERATION

Often, we do not want to make functional form assumptions about the data we analyze. We may want to fit a regression of an outcome on a set of regressors and be agnostic about the functional form of the regressors. Spline basis functions are flexible approximations to the functional form of the regressors. We may also want to visualize the relationship between an outcome and a regressor or between variables. We may use splines to visualize this relationship without claiming linearity or other functional forms.

In Stata 18, you can use the new makespline command to generate B-spline, piecewise polynomial spline, and restricted cubic spline basis functions from a list of existing variables. For example, we could type

. makespline bspline x1 x2 x3 x4 ...x100

to form 100 third-order B-spline basis functions, one for each variable from x1 to x100. We can now use any of the basis functions to fit a model and be agnostic about the relationship of the covariates and an outcome of interest. Or we could visualize the relationship of the outcome of interest and any of the basis function components that makespline generated.

…more

CORRECTED AIC AND CONSISTENT AIC

By popular request, the existing estat ic and estimates stats commands now support two new model-selection criteria: corrected Akaike information criterion (AICc) and consistent AIC (CAIC). The new option all displays all available information criteria. The new option df() specifies the degrees of freedom to compute the information criteria.

…more

INSTRUMENTAL-VARIABLES FRACTIONAL PROBIT MODEL

Fractional outcomes are common. You might be modeling participation rates in a 401(k) pension plan, the pass rate on standardized tests, expenditure shares, or the like.

Fractional response models are a flexible and intuitive way to model outcomes that lie between 0 and 1. They do not have the problem of linear models that will yield predictions outside 0 and 1 or the problem of log-odds models that are undefined at 0 and 1. Fractional response models can be fit using the fracreg command.

What if you are concerned that one or more of your model covariates are endogenous? With the new ivfprobit command, you can fit a model for a fractional dependent variable and account for endogeneity in one or more of the covariates.

…more

INSTRUMENTAL-VARIABLES QUANTILE REGRESSION

When we want to study the effects of covariates on different quantiles of the outcome, we use quantile regression. But what if we suspect that a covariate is endogenous? The new ivqregress command models quantiles of the outcome and, at the same time, controls for problems that arise from endogeneity.

…more

STATA GRAPHS HAVE AN ALL-NEW STYLE

Stata has an all-new default graph style.

…more

GRAPH COLORS BY VARIABLE

Want colors of the points in your scatterplot to reflect age groups? Or want the color of bars in your bar graph to reflect income level? Or want the colors of dots in your dot plot to reflect health status?

In Stata 18, the new colorvar() option allows many twoway plots to vary the color of markers, bars, and more based on the values of a variable.

…more

ALIAS VARIABLES ACROSS FRAMES

Stata supports multiple datasets in memory; each dataset resides in a frame. In Stata 18, you can now work with variables from different frames as if they exist in one.

When datasets are related, you can link their frames by using the frlink command to identify the variables that match the observations in the current frame with observations in the related frame.

Alias variables, created by the new fralias add command, define references to variables in linked frames. These variables take up very little memory because the observations are actually stored in another frame.

Stata treats alias variables like any other variable in your dataset, with the exception that you are not allowed to change their values. For a given alias variable, if you change the corresponding variable’s values in the linked frame, the changed values are automatically available the next time you use the alias variable.

…more

SAVING, USING, AND DESCRIBING A SET OF FRAMES

You work with multiple datasets in memory, also known as frames. When those datasets are related—perhaps they are used in the same project or linked to each other—you can now bundle them in a frameset. Save all of the datasets in one file. Use them all together later.

…more

BOOST-BASED REGULAR EXPRESSIONS

Regular expressions are powerful tools for working with string data. Stata’s regular expressions have become even more powerful, with more features, in Stata 18.

…more

VECTORIZED NUMERICAL INTEGRATION

Numerical integration is used in many computations of integrals when the analytic solutions are not available or difficult to calculate. Vectorized numerical integration approximates a vector of univariate numerical integrations simultaneously.

Mata’s new class, QuadratureVec(), is functionally the same as Quadrature(), except that it handles a vector of integration problems more conveniently. More precisely, QuadratureVec() approximates a vector of univariate integrals numerically by the adaptive Gauss–Kronrod method (the adaptive Simpson method is also provided for comparison).

QuadratureVec() is used in the same way as Quadrature() in only four steps, namely, creating an instance of the class QuadratureVec(), specifying the evaluator functions, setting the limits, and performing the computations.

…more

NEW REPORTING FEATURES

Reproducible reports allow us to streamline the process of presenting our findings as our analyses change. Whether the direction of our work changes or we implement feedback from our peers, creating a report with the findings of our research is rarely a one-time task. Stata’s reproducible reporting features allow us to easily modify and adapt our reports as our analyses change.

In Stata 18, we have added features for putdocx and putexcel that allow you to further customize your reproducible reports. Now you can include headers, footers, and page breaks with putexcel. You can also freeze a row or column in the worksheet; this allows you to maintain information from that row or column in view, while scrolling through the rest of the sheet. Additionally, you can create a named cell range to simplify working with formulas. We have also added support for bookmarks with putdocx; simply format your text as a bookmark, and link to it as needed. Additionally, when adding an image to a .docx file, you can now specify alternative text for the image to be read by voice software.

The dtable command is another new reporting feature in Stata 18. Learn more here about how you can use it to easily create a table of descriptive statistics, often called a “Table 1”.

…more

DO-FILE EDITOR ENHANCEMENTS

Automatic backups and syntax highlighting of user-defined keywords are now available in Stata’s Do-file Editor.

Automatic backups. Documents that are open in the Do-file Editor are periodically saved to a backup file on disk. This includes new documents that haven’t been saved to disk yet. If your computer were to lose power or crash before you had a chance to save the changes to your document, your unsaved changes can still be recovered. To recover your unsaved changes, open your document in the Do-file Editor again. If a backup file is found in the same location as your document, you will be prompted to recover the backup file or open the document that was last saved to disk. Recovering the backup file will simply load it into the Do-file Editor; it will not overwrite the document that was saved to disk unless you choose to do so.

Syntax-highlight user-defined keywords. Stata’s Do-file Editor now includes the ability to syntax highlight user-defined keywords. This will allow you to syntax highlight your favorite community-contributed commands. You simply create a specially named keyword definition file containing a list of keywords, and Stata will syntax highlight those keywords using a settable color and font styles such as bold or italics. You can even create a global keyword definition file that can be shared to all users of the same computer. Each user can still create their own local keyword definition file, and the keywords from both the global file and the local file will be loaded into the Do-file Editor.

…more

DATA EDITOR ENHANCEMENTS

The Data Editor has been rewritten for Stata 18. Users of previous versions of Stata will find the new Data Editor very familiar, but Stata 18 includes a host of new features, including pinnable rows and columns, resizable cell editors, tooltips for truncated text, the ability to show variable labels in the column headers, and proportional-width fonts.

…more

MORE NEW FEATURES IN STATA 18

• GENERAL STATISTICS
• CAUSAL INFERENCE
• META-ANALYSIS
• SURVIVAL ANALYSIS
• MULTIVARIATE STATISTICS
• GRAPHICS
• DATA MANAGEMENT
• FUNCTIONS
• PROGRAMMING
• MATA
• INTERFACE
• EVEN MORE

ADDED SINCE RELEASE DATE

• DATA MANAGEMENT
• GRAPHICS
• REPORTING
• INTERFACE