I demonstrate how to add custom labels for significant coefficients and make targeted style edits to cells in the table using the following commands:

collect get collect dir collect dims collect levelsof collect label list collect label values collect layout collect query header collect style header	collect style showbase collect style row collect style cell collect query column collect style column collect style stars collect query column collect preview etable

I begin with a description of what constitutes a collection and how items (numeric and string results) in a collection are tagged (identified) and conclude with a simple workflow to enable users to build their own custom tables from estimation commands. This presentation motivates the construction of estimation tables and concludes with the convenience command etable.

Using the recent Stata/Python integration platform(), introduced in Stata 16, these commands provide hyper-parameters’ optimal tuning via K-fold cross-validation using grid search. More specifically, they make use of the Python scikit-learn API to carry out both cross-validation and outcome/label prediction.

It relies on the estimate of the propensity score and uses regressions in subclasses (blocks) of the propensity score. The ATT is given by estimates within-block averaged for the number of treated units in each block. In the case of ATE, the estimates are averaged for the number of units (treated and untreated) in each block.

These models extend finite mixture models by allowing the researcher to model the marginal distribution of regression covariates along with the conditional distribution. The attention on CWMs is increasing; indeed, software for estimating these kinds of models is available to R users but not for Stata users. Thus, the aim of this presentation is to introduce the Stata package cwmglm. This package extends the capabilities of fmm by introducing more advanced mixture models based on maximum likelihood estimation and the expectation maximization (EM) algorithm.

cwmglm allows users to fit CWMs based on the most common generalized linear models (GLM) with random covariates. The supported GLM families are Gaussian, Poisson and binomial, while the allowed marginal distributions for the covariates are multivariate normal, multinomial, binomial, and Poisson. cwmglm extends the current capabilities in the estimation of CWMs by allowing users to evaluate model fit by introducing the generalized determination coefficients and by incorporating bootstrap-based inference. These features are not available in the current version of the R-package software for CWMs.

Furthermore, cwmglm allows one to estimate parsimonious models of Gaussian distributions. This approach is based on assuming the correlation structure between concomitants within multivariate Gaussian mixture components and on the equality/inequality of variance–covariance matrices between components. Fourteen parsimonious models are possible by exploiting the eigenvalue decomposition of the variance–covariance matrix. Parsimonious mixtures of multivariate Gaussian distributions can be used to model random covariates within CWM-GLM or as stand-alone models (mixture of multivariate Gaussians with defined covariance matrix). This feature is completely new for Stata users because it is not allowed by gsem and fmm. Last, the flexibility of cwmglm allows one to estimate the “canonical” finite mixture of regressions.

Stacking combines multiple supervised machine learners—the “base” or “level-0” learners—into a single learner. The currently supported base learners include regularized regression, random forest, gradient boosting, support vector machines and feed-forward neural nets (multilayer perceptron). pystacked can also be used with a “regular” machine-learning program to fit a single base learner and thus provides an easy-to-use API for scikit-learn’s machine-learning algorithms.

Five different models are supported, allowing for binary or continuous treatment variables and endogeneity. ddml supports a variety of different ML programs, including lassopack and pystacked.

You can use lasso when you want to control for many potential covariates. With standard treatment-effects models, there is an intrinsic conflict between two required assumptions. The conditional independence assumption is likely to be satisfied with many variables in the model, while the overlap assumption is likely to be satisfied with fewer variables in the model. This presentation shows how to overcome this conflict by using Stata 17’s telasso command.

telasso estimates the average treatment effects with high-dimensional controls while using lasso for model selection. This estimator is robust to the model-selection mistakes. Moreover, it is doubly robust, so only one of the outcome or treatment model needs to be correctly specified.

Although the estimation of model parameters does not differ from the bivariate case, the existing commands biprobit and cmp do not consider the structural model’s recursive nature for postestimation commands. rbiprobit estimates the model parameters, computes treatment effects of the first dependent variable, and gives the marginal effects of independent variables. In addition, marginal effects can be decomposed into direct and indirect effects if covariates appear in both equations. Moreover, the postestimation commands incorporate the two community-contributed goodness-of-fit tests scoregof and bphltest. Dependent variables of the recursive probit model may be binary, ordinal, or a mixture of both. I present and explain the rbiprobit command and the available postestimation commands using data from the European Social Survey.

The package uses an Excel file to store all metadata related to a dataset. This is particularly useful to edit and modify metadata outside of Stata, and also to deal with datasets stored in non-Stata format. The presentation will focus on the most important features of the package, namely on how to extract metadata from data in memory, perform consistency checks on the metadata, apply metadata to data in memory, and compare and combine metadata from two datasets.

It uses MCMC estimation methods (LeSage and Pace 2009) to produce estimates of endogenous peer effects, as well as own-node (direct) and cross-node (indirect) partial effects, where nodes correspond to cross-sectional units of observation. nwxtregress is designed to handle unbalanced panels of economic and social networks as in Grieser et al. (2021). Networks can be directed or undirected with weighted or unweighted edges, and they can be imported in a list format that does not require a shapefile or a Stata spatial weight matrix set by spmatrix. Finally, the command allows for the inclusion or exclusion of contextual effects. To improve speed, the command transforms the spatial weighting matrix into a sparse matrix. Future work will be targeted toward improving sparse matrix routines, as well as introducing a framework that allows for multiple networks.

When combined, chronic diseases create additional challenges to patient care because clinical trials usually exclude patients with coexisting conditions; therefore, most guidelines do not provide recommendations for patients presenting with multiple diseases. With worldwide life expectancy increasing from 45.7 years in 1950 to 72.6 years in 2019 and 20% of people aged ≥ 65 years in Europe in the same year, understanding the patterns and risk factors of multimorbidity has become of great relevance for public health. Multistate models are a well-suited statistical framework to address this problem.

Because of its impact on a company’s growth, line managers are strongly interested in knowing which factors can increase NPS by increasing promoters and decreasing detractors. NPS key drivers’ analysis (NPS KDA) can be a suitable tool for this task. A KDA may be conducted by implementing different statistical approaches for identifying those factors or drivers with a significant impact on a specific outcome variable. In the context of NPS KDA, the regression models for ordinal outcomes represent a statistical approach for identifying those significant customer experience (CX) attributes that can drive customer status (CS) from detractors to promoters, leading companies to design appropriate improvement strategies, involving those facets of product or service with the highest improvement priority.

In this presentation, the NPS KDA has been conducted by implementing in Stata two special cases of the generalized ordered logit models, the proportional odds model (POM) and the partial proportional odds model (PPOM), where the dependent variable CS was modeled as a function of different CX attributes.

I used the INPS database relating to employees; the time interval considered was 2009–2018, and the variable credit difference was examined, which is a measure of the salary that workers have not received because of absence from work.

Then, the existence of geographical influence between Italian regions was verified through the creation of maps using Stata. From which other variables available, a new variable was created, measures the number of absences of workers for each region. The creation of maps enabled us to see the Italian regions where workers are more absent. Looking only at the sectors most affected by climate change, we see the results vary.

Finally, only absences due to sickness and injury were observed, because they could be caused by climate change and extreme weather events. By observing the outliner values of the variable that measures absences from work, we found that extreme weather events actually occurred in the month and in the region in which the value far from the average was recorded.