Abstract: Part of the art of coding is writing as little as possible to do as much as possible. In this presentation, I expand on this truism and give examples of Stata code to yield tables and graphs in which most of the real work is delegated to workhorse commands. In tabulations and listings, the better known commands sometimes seem to fall short of what you want. However, some preparation commands (such as generate, egen, collapse, or contract) followed by list, tabdisp, or tab can get you a long way. In graphics, a key principle is that graph twoway is the most general command even when you do not want rectangular axes. Variations on scatter and line plots are precisely that, variations on scatter and line plots. More challenging illustrations include commands for circular and triangular graphics, in which x and y axes are omitted with an inevitable but manageable cost in recreating scaffolding, titles, labels, and other elements. The examples range in scope from a few lines of interactive code to fully developed programs. This presentation is thus pitched at all levels of Stata users.

Abstract: Precise and detailed data documentation is essential for the secondary analysis of scientific data, whether they are survey or official microdata. Among the most important metadata in this perspective are variable and category labels and frequency distributions and descriptive statistics. To generate and publish these metadata from Stata datafiles, an efficient export interface is essential. It must be able to handle large and complex datasets, account for the specifics of different studies, and generate flexible output formats (depending on the requirements of the documentation system). As a solution to the problem described above, we present the process developed in the GML (German Microdata Lab) at GESIS. In the first step, we show how an aggregated file with all required metadata can be generated from the microdata. In the second step, this file is transformed into a standardized DDI format. Additionally, we will present the implementation for MISSY (the metadata information system for official microdata at GESIS), which includes some practical additions (for example, communication with the MISSY database to retrieve existing element identifiers, writing an output tailored to the MISSY data model).

Abstract: An Agent Based Model (ABM) is a simulation in which agents that each follow simple rules interact with one another and thus produce an often surprising outcome at the macro level. The purpose of an ABM is to explore mechanisms through which actions of the individual agents add up to a macro outcome by varying the rules that agents have to follow or varying with whom the agent can interact (for example, varying the network).

A simple example of an ABM is Schelling's segregation model, in which he showed that one does not need racists to produce segregated neighborhoods. The model starts with 25 red and 25 blue agents, each of which live in a cell of a chessboard. They can have up to 8 neighbors. In order for an agent to be happy, they need to have some, e.g. 30%, agents in the neighborhood of the same color. If the agent is unhappy, they will move to another empty cell that will make them happy. If we repeat this until everybody is happy or nobody can move, we will often end up with segregated neighborhoods.

Implementing a new ABM will always require programming, but a lot of the tasks will be similar across ABMs. For example, in many ABMs the agents live on a square grid (like a chessboard), and can only interact with their neighbors. I have created a set of Mata functions that will do those tasks, and someone can also import their own ABM. In this presentation, I will illustrate how to build an ABM in Mata with these functions.

Abstract: Traditional fit measures like RMSEA, TLI, or CFI are based on noncentral chi-square distribution assuming the multinormal distribution of the observed indicators (Jöreskog 1970). If this assumption has violated programs like Stata, EQS or LISREL calculate the fit indices using the Sattora-Bentler correction. It rescales the likelihood ratio chi2 test statistics of the baseline and the hypothesized model (Satorra & Bentler 1994, Newitt & Hancock 2000). Brosseu-Liard et al. (2012, 2014) and Savalei (2018) showed two results in their simulation studies with nonnormal data: First, they demonstrated that the ad hoc nonnormality corrections of the fit indices provided by the SEM software made the fit worse, better, or unchanged compared with their uncorrected counterparts. Second, the authors proposed new robust versions of RMSEA, CFI, and TLI that performed very well in their simulation studies. They systematically varied the sample size, the extent of misspecification, and nonnormality. Therefore, the same rules of thumb or criteria that are used for normal distributed data can be applied to assess the fit of the structural equation model.

My robust_gof.ado ado-file stimates the robust RMSEA, CFI, and TLI fit measures using the corrections proposed by Brosseu-Liard et al. and Savalei. It also estimates a 90% confidence interval for the root mean squared error of approximation. robust_gof.ado can be executed after the sem command with the vce(sbentler) option and estat gof, stats(all) as a postestimation command by simply typing robust_gof. It returns the estimated fit indices and scalars as r containers. I will present a survey example of islamophobia analysis in Germany to demonstrate the usefulness of robust_gof.ado.

References:

Asparouhov, T., and B. Muthén. 2010. Simple second order chi-square correction. MPLUS Working papers.

Borsseau-Liard, P.E., V. Savalei, and L. Li. 2012. An investigation of the sample performance of two nonnormality corrections for RMSEA. Multivariate Behavioral Research 47: 904–930.

Borsseau-Liard, P.E, and V. Savalei. 2014. Adjusting incremental fit indices for nonnormality. Multivariate Behavioral Research 49: 460–470.

Jöreskog, K.G. 1970. A general method for analysis of covariance structures. Biometrika 57: 239–251.

Jöreskog, K.G., U.H. Olsson and F.Y. Wallentin. 2016. Multivariate Analysis with LISREL.

Abstract: The Oaxaca–Blinder (Oaxaca 1973) decomposition approach has been widely used to attribute group-level differences in an outcome to differences in endowment, coefficients, and their interactions. This method has been implemented for Stata in the popular oaxaca command for cross-sectional analyses (Jann 2008). However, in the last decades, research questions are more often focused on the decomposition of group-based differences in change over time, such as diverging income trajectories and decomposition of change in differences between groups, for example, change in the gender pay gap. Decomposition analyses can also be extended to longitudinal data by repeated cross-sectional decompositions and time point-specific decomposition of group-level differences based on latent growth curve models. We propose to unify these different research interests under a more general longitudinal perspective that has each of the applications as a special case of the Oaxaca–Blinder decomposition. We present this general view, give examples of applied research questions that can be answered within the framework, and propose a first version of the command xtoaxaca, which works as a postestimation command in Stata to maximize flexibility in modeling and forms of longitudinal decompositions according to the user's preferences.

References:

Jann, B. (2008). The Blinder–Oaxaca decomposition for linear regression models. The Stata Journal. 8: 453–479.

Oaxaca, R. 1973. Male-female wage differentials in urban labor markets. International Economic Review 14: 693–709.

Abstract: Linear fixed-effects estimators (first differences, within transformation) are workhorses of applied econometrics because they straightforwardly allow for eliminating unobserved time-invariant individual heterogeneity that otherwise may cause a bias. However, I show that these popular estimators are biased and inconsistent when applied in a discrete time hazard setting, that is, with the outcome variable being a binary dummy indicating an absorbing state. I suggest an alternative, computationally simple adjusted first-differences estimator. This estimator is shown to be consistent in the considered nonrepeated event setting under the assumption of unobserved time-invariant individual heterogeneity being uncorrelated with the changes in the explanatory variables. Using higher-order differences instead of first differences allows for consistent estimation under weaker assumptions. In this presentation, I introduce the new community-contributed command xtlhazard, which implements the suggested estimation procedure in Stata.

Abstract: In this presentation, I will present two new Stata commands to produce heat plots. Generally speaking, a heat plot is a graph in which one of the dimensions of the data is visualized using a color gradient. One example of such a plot is a two-dimensional histogram in which the frequencies of combinations of binned X and Y are displayed as rectangular (or hexagonal) fields using a color gradient. Another example is a plot of a trivariate distribution where the color gradient is used to visualize the (average) value of Z within bins of X and Y. Yet another example is a plot that displays the contents of a matrix, say, a correlation matrix or a spacial weights matrix, using a color gradient. The two commands I will present are called heatplot and hexplot.

Abstract: Four years ago, I first suggested extending Stata's label commands to manipulate variable labels and value labels in a more systematically. By now, I have refined my earlier approach and released a new suite of commands, elabel, that facilitate these everyday data management tasks. In contrast to most existing community-contributed commands to manipulate labels, elabel does not focus on solving specific problems. Combined with any of Stata's label commands, it addresses any problem related to variable and value labels. elabel accepts wildcard characters in value label names, allows referring to value labels via variable names, selects subsets of integer to text mappings, and applies any of Stata's functions to define new or modify existing labels. I demonstrate these features drawing on various examples and show how to write new ado-files to further extend the elabel commands.

Abstract: The Global Multidmensional Poverty Index is a cross-country poverty measure published by the Oxford Poverty and Human Development Initiative since 2010. The estimation requires household survey data because multidimensional poverty measures seek to exploit the joint distribution of deprivations in the identification step of poverty measurement. Moreover, analyses of multidimensional poverty draw on several aggregate measures (for example, headcount ratio, intensity), and on dimensional quantities (for example, indicator contributions). Robustness analyses of key parameters (for example, poverty cutoffs and weighting schemes) further increase the number of estimates.

During the 2018 revision, for the first time figures for 105 countries were calculated in one round. For a large-scale project like this, a clear and efficient workflow is essential. This presentation introduces key elements of a workflow and presents solutions with Stata for particular problems, including the structure of a comprehensive results file, which facilitates both analysis and production of deliverables, the usability of the estimation files, the collaborative nature of the project, the labelling of 1200 subnational units, and the documentation of code and decisions. This presentation seeks to share the gained experience and to subject both the principal workflow and selected solutions to public scrutiny.

Abstract: Discrete choice models are used across many disciplines to analyze choices made by individuals or other decision-making entities. Stata supports many discrete choice models, such as multinomial logit and mixed logit models. While applying these models to a given dataset can be straightforward, it is often challenging to interpret their results. In this presentation, I will provide an overview of Stata's discrete choice modeling capabilities and show how to use postestimation commands to get the most out of these models and their interpretation.

Abstract: Stata developers present will carefully and cautiously consider wishes and grumbles from Stata users in the audience. Questions, and possibly answers, may concern reports of present bugs and limitations or requests for new features in future releases of the software.