One of the reasons for this increased interest is that a wide variety of models can be accommodated in this alternative regression approach. For example, a variety of multilevel, multiequation, ad hoc Bayesian models can be fit by combining prior functions and likelihood functions that are already available in Stata.

In this presentation, I will provide an intuitive introduction to Bayesian analysis concepts and demonstrate how to fit Bayesian models. I will also discuss some of the postestimation and prediction tools available in Stata.

You can use lasso to perform model selection and prediction for continuous, binary, and count outcomes, plus use state-of-the-art methods to make inferences on the variables of interest, while lasso selects the control variables.

This presentation provides an introduction to lasso using Stata. In lasso for prediction, some conceptual aspects of the methods will be addressed. An example of continuous variable prediction comparing lasso, elastic net, square-root lasso, and ordinary least squares (OLS), among other things, will be presented, showing how to use the options to select the value of the lasso penalty parameter. In lasso for inference, some of the theory will be covered. Likewise, it will provide an example for a linear model using the double-selection, partialing-out, and cross-fit partialing-out lasso methods (also known as double machine learning), and finally, it will show how to customize individual lassos and how to use some control selection techniques.

to build epidemic curves, weekly endemic channels, and population pyramids of morbidity, mortality, and lethality. In addition, the COVID-19 data from Mexico and some data from other studies will be used to display and analyze comparisons of means.

In this presentation, I discuss the estimation of panel event-study models using the xtevent package. The construction of event-study graphs, the economic implications of different possible identification assumptions, and the use of different econometric estimators are also discussed.

However, compared with their peers, they continue to present disadvantageous results that worsen according to the city. Indeed, they continue to register lower employment rates, lower wages, and higher unemployment rates. Evaluating these differences is relevant because it allows proposing economic policies to solve the problems faced by women in the labor market. This presentation offers a theoretical-practical approach on the application of probit models to estimate unemployment functions and ATF models with Weibull or lognormal distribution to estimate unemployment duration equations. The estimations will be done with the support of Stata, one of the most complete, reliable, and easy to apply statistical software in the field of research.