We offer a new Stata command, predict_ldm, that can be used after the regress command to generate predicted values that always fall within the (0,1) interval. We show that, for many applications, these values are very close to those produced by logistic regression. We also explore applications where there are substantial differences between logistic predictions and those produced by predict_ldm. Finally, we show that the linear discriminant method can be used to substantially improve multiple imputations of categorical data based on the multivariate normal model. We are currently developing a new mi impute command to implement this method.

As of now, these commands are only suitable for a very limited set of approaches: mhbounds is suited for kth nearest neighbor matching without replacement and for stratification matching (Becker and Caliendo 2007) and rbounds is suitable only for one-to-one matching (Gangl 2004). The restriction to these approaches is a serious limitation to these commands. This presentation will describe adjustments to mhbounds that made it compatible with another approach to balancing observable characteristics: coarsened exact matching (Iacus, King, and Porro 2011). It will also discuss technical issues that future research should address if the command is to be expanded to allow other approaches, such as matching with replacement.

Specifically, we analyze a representative dataset of USA mortgage loans originated over the past 20 years that includes detailed loan-level information on monthly loan performance and other relevant loan and borrower characteristics. Our analysis and discussion illustrates how GSEM techniques can significantly impact every aspect of a model-driven risk management framework, from model development, documentation, and validation to model production, as well as to other, perhaps less obvious, aspects of model building like model risk management, enhanced team collaboration, minimization of proliferation of disparate datasets within projects, and the promotion of a holistic and collaborative approach to model building.

The older command eventstudy (Zhang et al. 2013) provides a comfortable graphical user interface (GUI) and good functionality for event studies that do not require hypotheses testing. The command estudy described in Pacicco et al. (2018) provides a comprehensive set of test statistics, but its application is restricted to single-day event studies, which represent a very small fraction of event studies conducted in accounting, finance, and management journals.

In this talk, I’ll provide an example of how to implement a common programming pattern from the computer science field and how it can solve this performance issue more effectively. Based on a set of scripts developed by Adam Nelson (https://github.com/adamrossnelson/StataIPEDSAll), I developed a solution (https://github.com/wbuchanan/ipeds) that uses the singleton pattern to reduce object instantiation and I/O operations over multiple calls in order to improve performance.

Although this Mata code is widely applicable, it is mainly designed for estimators that involve 2D integration at the observation level (for example, the likelihood function for a two-equation nonlinear regression system). The user inputs a vector-valued integrand function (for example, a vector of sample log-likelihood integrands) and a matrix of upper and lower limits for each of the two integration dimensions. The code outputs the corresponding vector of integrals (for example, the vector of observation-specific log likelihoods). To illustrate implementation, we estimate a bivariate seemingly unrelated 2D system of dispersion-flexible Conway–Maxwell Poisson regressions for the number of consultations in a two-week period with a 1) doctor and 2) non-doctor health professional, or both. The data were drawn from the 1977–1978 Australian health survey. Results from this model are juxtaposed with those from Conway–Maxwell and simple Poisson specifications in which possible cross-equation correlation is ignored.

Out of 8,418 bicycle-motor vehicle crash records from the UNC Highway Safety Research Center, 1,273 cases were evaluated. Injury severity is measured on an ordinal scale as minor, major, or severe. Stata's ordinal logistic regression was used to initially analyze potential factors associated with bicyclist injury severity, followed by generalized ordered logit (gologit) via the community-contributed ado-program gologit2 (Williams 2006). Generalized ordered logit relaxes the constraint that a variable has the same estimated coefficient throughout the range of injury severity. Statistical significance was linked to injury severity in the following variables: bicyclists 55 and older, the driver's speed, roadway features, day of week, light conditions, and season.

I present the new xtdpdgmm command, which addresses some shortcomings of xtabond2 and adds further flexibility to the specification of the estimators. In particular, it allows one to incorporate the Ahn and Schmidt (1995) nonlinear moment conditions that can improve the efficiency and robustness of the estimation. Besides the familiar one-step and two-step estimators, xtdpdgmm also provides the Hansen, Heaton, and Yaron (1996) iterated GMM estimator.

Using Monte Carlo simulations, we compare the performance of six proposed diagnostics that make use of statistical tests available in the literature such as Lagrange multipliers, Lagrange ratios, and F tests. We find that these six pretest estimators are a viable alternative to estimate panel-data models with unobserved cluster effects, in the sense that they achieve empirical sizes very close to the ones obtained using an estimator of the variance as if we knew the true data-generating process.

The procedure estimates the same specification n times, keeping constant the same dependent variable and an optional list of control variables. However, at each repetition, the procedure includes only one of the n-candidate variables in the specification, (in addition to the list of fixed control variables) until each one of the candidate variables listed by the user in the syntax has been included and evaluated. For each candidate variable, the procedure estimates seven parameters and statistical criteria. xtselmod helps us to select the best specification between all possible combinations of a defined set of explanatory variables. It is closely related to the command xtselvar and relies heavily on the Stata command tuples. Given n possible explanatory variables, the procedure estimates 2^n - 1 different specifications, one per each possible combination. Then, for each one of them, the procedure estimates a set of five statistical criteria. More specifically, xtselvar estimates seven statistics per variable (Coefficient, t-statistic, Adj. R2, AIC, BIC, U-Theil in time-series, U-Theil in cross-individual), while xtselmod estimates only the last five per specification. The procedures then rank each variable or specification according to those last five statistical criteria and generate one ranking for each one of them. It also computes a composite ranking summarizing all five criteria. It finally sorts all candidate variables or specifications according to the selected ranking, which by default is the composite ranking. The out-of-sample evaluation of each candidate variable and specification is performed based on the commands xtoos_t and xtoos_i, which need to be installed in Stata to be able to execute the procedures. xtselvar and xtselmod allow one to choose weights for each one of the five criteria used to compute the composite ranking. They also allow one to rank the variables and specifications according to a specific criterion of preference. For instance, if the primary objective of the estimation is to obtain the most accurate prediction of the dependent variable, one could choose to rank the candidate variables and specification according only to their forecasting ability, that is, according to the estimated U-Theil in its time-series dimension. The procedures allow one to choose different estimation methods, including some dynamic methodologies, and could also be used in a dataset with only time-series observations. When the specification includes lags of the dependent variable, the procedure is able to automatically generate dynamic forecasts for the out-of-sample evaluation performance. In the case of the out-of-sample evaluation in a time-series dimension, they allow one to choose a specific horizon h at which to evaluate the forecasting performance of the model, including the candidate variable and specification. It also allows one to estimate the forecasting performance from horizon t 1 until t h. xtselmod adjusts the Stata command tuples so that it allows time-series operators like lags, leads, and differences. Importantly, it also allows one to choose and use the conditionals option of the command tuples, using the same structure and syntax.

An alternative to fully nonparametric models are semiparametric models that combine the flexibility of nonparametric regressions with the structure of standard models. This presentation describes the estimation of a particular type of semiparametric modes known as smooth varying-coefficient models (Hastie and Tibshirani 1993), based on kernel regression methods, using a new set of commands in vc_pack. These commands aim to facilitate bandwidth selection and model estimation and create visualizations of the results.

The aim of this presentation is to use Microsoft Excel jointly with Stata to facilitate data governance and manipulation in large-scale datasets. Method: This presentation will focus on three different ways that Excel can be used as a supportive tool to facilitate and expedite the data manipulation, analysis, interpretation, and reporting in Stata, with a focus on large datasets with many variables. First, Excel will be used as an interactive data dictionary tool to select and keep track of variables included in various analysis stages. Second, Excel commands and features will be used to generate batch commands to perform repeated variable transformation and conditional data manipulation or analysis in Stata. Finally, Stata output tables will be imported to Excel to further customize preparation and reporting. Each of these three categories of tasks will be supported by at least one example from a dataset with many variables. Conclusion: Using Microsoft Excel features and commands jointly with Stata can benefit data scientists and researchers by improving efficiency and productivity through saving time and providing a comprehensive picture of a dataset.

The command allows users to display a list of the surveys associated with that account, to download surveys with IDs retrieved from that list, and to apply variable and value labels. In this presentation, we will cover the functions of the .ado and demonstrate applied examples. The program uses cURL requests to fetch data from the Qualtrics API and import them into Stata, processes the files in Stata, then passes them back to the Qualtrics API or applies them to the downloaded data (such as variable and value labels and question stems). The ability to script the fetching and cleaning from a third-party survey platform opens up other possibilities, such as up-to-date results dashboards or response rates websites, which we will demonstrate in our presentation.

1. Stronger family bonds and marriage relationship have positive effects on children's earnings skills and economic status. 2. The influence goes through the channels of the investments in children's education and the intergenerational endowment inheritability. 3. Successful marriage leads to intergenerational relative earnings improvement. I use PSID data to connect parents and their children. Two-stage-least-squares estimations help to alleviate the endogeneity problem of the explanatory variable of interest, parent's marital status. Preliminary results show the following: 1. The direct effects of parents' marital status on children's earnings reflect the factors that are related to the unobservable family endowment inheritance. 2. The education premium is affected by parents' marital status. Earnings returns to schooling are higher for workers who grew up in families with married parents than in families with parents who experienced divorce or were ever single. 3. Marriage has more positive effects on sons' earnings than on daughters'.

using victimization surveys and censuses of police stations and municipalities and using the newly implemented machine-learning techniques in Stata combined with propensity score matching. Results: reduction of 3 percentage points (pp.) in the probability of trusting in the police and Serenazgo in the short term and 2 pp. in judicial power in the long term. Female victims would lose more confidence in Serenazgo and the Public Ministry. Robustness in the presence of unobservables, different pairings, and falsification tests, which would suggest potential causal character.