Abstract: Social network analysis is one of the most rapidly growing fields of the social sciences. Social network analysis focuses on the relationships that exist between individuals (or other units of analysis), such as friendship, advice, trust, or trade relationships. Network analysis is concerned with the visualization and analysis of network structures, as well as with the importance of networks for individuals' propensities to adopt different behaviors. Until now, such analyses have been possible to perform using specialized software for network analysis only. This tutorial introduces the nwcommands, a software suite with over 90 Stata commands for social network analysis. The software includes commands (and dialog boxes) for importing, exporting, loading, saving, handling, manipulating, replacing, generating, visualizing, and animating networks. It also includes commands for measuring various properties of the networks and the individual nodes and for detecting network patterns and measuring the similarity of different networks, as well as advanced statistical techniques for network analysis, including MR-QAP and ERGM.

Abstract: The package npf offers five new Stata commands that estimate and provide statistical inference in nonparametric frontier models. All commands are realized using fast plugins compiled from C codes. The first two commands, tenonradial and teradial, estimate data envelopment models where nonradial and radial technical efficiency measures are computed (Färe 1988; Färe and Lovell 1994; Färe et al. 1994). Technical efficiency measures are obtained by solving linear programming problems. The rest of the commands, teradialbc, nptestind, and nptestrts, give tools for making statistical inference regarding radial technical efficiency measures (Simar and Wilson 1998, 2000, 2002). We demonstrate the capabilities of the new commands using small and large samples. Finally, a small empirical study of productivity growth is performed.

Abstract: Stata 14 provides a suite of commands for performing Bayesian analysis. Bayesian analysis is a statistical paradigm that answers research questions about unknown parameters using probability statements. For example, what is the probability that a person accused of a crime is guilty? What is the probability that there is a positive effect of schooling on wage? What is the probability that the odds ratio is between 0.3 and 0.5? And many more. In my presentation, I will describe Stata’s Bayesian suite of commands and demonstrate its use in various applications. performed.

Abstract: Software documentation is very time consuming but inevitable. Stata includes two programming languages for developing statistical packages and new commands. Nevertheless, the package documentation has been a manual process, especially for generating Stata help files in sthlp format, which requires writing the help file using SMCL markup language. A better approach to documenting packages and writing help files is provided by the MarkDoc package, if the documentation is written within the program. MarkDoc processes the documentation and not only generates Stata help files dynamically but also can use the same source to produce PDF, Microsoft Word, or HTML documentation. The presentation discusses the benefits of package documentation and its applications for front-end Stata users who program with Stata or Mata. performed.

Abstract: At the 2009 meeting in Bonn, I presented a new Stata command called texdoc. The command allowed weaving Stata code into a LaTeX document, but its functionality and its usefulness for larger projects was limited. In the meantime, I heavily revised the texdoc command to simplify the workflow and improve support for complex documents. The command is now well suited, for example, to generate automatic documentation of data analyses or even to write an entire book. In this talk, I will present the new features of texdoc and provide examples of their application.

Abstract: Both the margins postestimation command, which obtains marginal effects or adjusted predictions, and the mi suit, which implements multiple imputation methods as a sophisticated way for dealing with missing data, are powerful features introduced in Stata 11 and further enhanced in subsequent releases. Unfortunately, it is not (yet) possible to combine their capabilities in official Stata. In this talk, I discuss an approach for estimating marginal effects in multiply imputed datasets, first suggested by Isabel Cañette and Yulia Marchenko on Statalist. Drawing on their original specific example, I briefly review the underlying theoretical considerations and demonstrate how the mimrgns command generalizes the outlined technique in a user-friendly way.

Abstract: Since the early nineties, logistic regression for binary, ordinal, and nominal dependent variables has become widely spread in the social sciences. Nevertheless, there is no consensus on how to assess the fit of these models corresponding to practical significiance. A lot of pseudo coefficients of determination have been proposed but seldom used in applied research. Most of these pseudo-R²s follow the principle of the proportional reduction of error comparing the likelihood, log likelihood, or the precision of prediction with those of a baseline model including the constant only. Alternatively, McKelvey and Zavoina (1975) have proposed a different one estimating the proportion of explained variance of the underlying latent dependent variable. Summarizing the Monte Carlo studies of Hagle and Mitchell (1992), Veall and Zimmermann (1992,1994), and Windmeijer (1995), I show that the McKelvey and Zavoina pseudo-R² is the best one to evaluate the fit of binary and ordinal logit and probit models. Applying the assumption of identical independent distributed errors, I also propose a generalization of the McKelvey and Zavoina pseudo-R² to the multinomial logistic regression assessing the fit of each binary comparison simultaneously. The usefulness of this concept is demonstrated by applied data analysis of an election study with Stata using a user-written mzr2.ado file.

Abstract: This presentation illustrates practical uses of influence functions (IF) in Stata. First (and most obviously), inspection of IFs helps detecting influential sample observations. I show how this can be done in practice and how similar this is to examining jackknife replicates. I illustrate how looking at the influence function helps one understand the underlying structure of the measures being estimated. Second, IFs make it easy to calculate (asymptotic) standard errors and confidence intervals for a wide range of statistics. I illustrate how this can be done in Stata with the total command to account for complex survey design easily. Third and finally, application of "recentered influence function (RIF) regression" has recently been advocated to approximate the impact of covariates on (unconditional) distribution statistics. I demonstrate this use of IFs in Stata and discuss interpretation of RIF regression model coefficients. Empirical applications pertain to income distribution analysis.

Abstract: Sequences are entities built by a limited number of elements that are ordered in a specific way. A typical example is human DNA, where the elements adenine, cytosine, guanine, and thymine (the organic bases) are ordered into a sequence. Other sequences are words, which are built by letters that appear in a specific order, or careers of employers, which are built by specific job positions ordered along time. The user-written SQ-Ados, published in 2006, provide a number of tools to describe sequences and to measure the similiartiy between pairs of sequences. Since its first publication, the SQ-Ados have been continiously updated with new functionalties. This presentation gives an overview of existing programs and discusses the most recent additions, including functions for string comparisions, nearest neighbor identification, and a simple interface to the plugins for sequence analysis developed by Brandan Halpin.