Simulation studies are computational experiments used to evaluate the properties of statistical methods-typically methods for design or analysis. Used well, simulation studies are an invaluable tool. However, they are often complex, with multiple data-generating mechanisms, multiple estimands, and multiple methods of analysis. When one analyzes the results of such simulation studies, errors are often made and sometimes missed. Getting the analysis right can be delicate and time-consuming. In this article, we introduce siman, a suite of programs that offers data manipulation, exploration, visualization, and analysis of the results of complex simulation studies.
In this article, we introduce the new community-contributed commands xtdhazard and cfbinout . The former implements the own-differences instrumental-variables estimator proposed by Farbmacher and Tauchmann (2023, Econometric Reviews 42: 635-654) for dealing with time-invariant unobserved heterogeneity in the discrete-time hazard model. cfbinout is called by xtdhazard if a nonlinear discrete-time hazard model is specified. cfbinout can also be used as a standalone command that generalizes ivprobit , twostep by allowing discrete endogenous regressors and link functions that are different from the normal link, specifically logit and complementary log-log. In terms of the underlying econometric theory, cfbinout is guided by Wooldridge (2015, Journal of Human Resources 50: 420-445). We illustrate the use of xtdhazard in an empirical example.
In this article, we present two commands— ccscc and sccintreg —that conduct parameter inference in moment-inequality models, which are defined by inequality moment conditions. Such models have gained popularity in the economics literature because of the flexibility they offer. Examples include missing- data models with nonrandom missingness and game-theoretic models with multiple equilibriums. Parameters in such models often cannot be consistently estimated. Nevertheless, valid confidence intervals can be constructed by inverting a hypothesis test for the compatibility of a parameter value with the model. The ccscc command implements the computationally easy tests proposed by Cox and Shi (2023, Review of Economic Studies 90: 201-228). The sccintreg command builds on ccscc to compute marginal confidence intervals for each parameter in an interval- outcome linear regression model. We demonstrate the use of our commands in two simulation examples.
Quantile-box plots as discussed here are variations on a plot originally proposed by Emanuel Parzen in 1979. The main ideas are 1) the quantiles of a batch of data for a single variable, meaning the order statistics or ordered values, are plotted vertically as point or marker symbols against fraction of the data or plotting position plotted horizontally; 2) a box showing median and quartiles is shown either in the same space or alongside. Optional choices include 3) plotting the quantiles on a transformed scale (for example, logarithmic); 4) plotting fraction of the data on a transformed scale (for example, normal quantile); 5) adding details in the tails of the box plot (for example, spikes extending to paired percentiles or quantiles); 6) adding indications of other summary measures (for example, means or geometric means shown as extra horizontal lines); 7) showing quantile traces as connected curves rather than a series of marker symbols; 8) smoothing of quantiles to reduce minor noise; 9) plotting quartile or midgap plots instead of more conventional box plots; and 10) showing confidence intervals for any suitable measure of level (location, central tendency) instead of median-quartile boxes.Quantile-box plots provide indications of the level, spread, and shape of distributions together with indications of detailed features whenever they occur, such as outliers, spikes, and gaps without values. They do not supersede other useful displays such as histograms or density estimates, but they do not entail possibly awkward choices of bin width or start or of kernel type and bandwidth, nor do they require tuning of such choices. Quantile plots show essentially the same information as plots of the cumulative distribution or its complement, which may well be preferred by researchers as already familiar or as conventional in their field. Although quantile-box plots for individual batches may be interesting or useful, their main value often lies in comparison of two or more batches of data.While it starts from Parzen's ideas, the discussion extends earlier and later. The history of quantile plots and box plots, usually under other names, is longer and more diverse than is often appreciated. The use of median-quartile boxes by Arthur L. Bowley around 1897 and by William C. Marshall in 1921 has escaped most reviews.In Stata terms, the community-contributed command, qplot, as updated in this issue, is a convenient workhorse for quantile and associated plots. The community- contributed commands pctilesets, quantilesets, and cisets, discussed in the previous issue (Cox, 2026, Stata Journal 26: 291-322), may be found to be convenient for calculation of quantile and other summaries. The official command egen continues to be useful for similar calculations. A strategy of divide and conquer-dividing the problem into steps of numerical calculation and graphical display-will often be helpful.
