Template-Type: ReDIF-Article 1.0 Author-Name: Harald Tauchmann Author-Workplace-Name: Friedrich-Alexander-Universität Erlangen-Nürnberg Author-Email: harald.tauchmann@fau.de Author-Person: pta144 Author-Name: Elena Yurkevich Author-Workplace-Name: Friedrich-Alexander-Universität Erlangen-Nürnberg Author-Email: elena.yurkevich@fau.de Title: xtdhazard and cfbinout: Using internal instruments for addressing unobserved heterogeneity in the discrete-time hazard model Journal: Stata Journal Pages: 325-366 Issue: 3 Volume: 26 Year: 2026 Month: September Abstract: 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. Keywords: xtdhazard, cfbinout, ivprobit, ivregress, control function, dis- crete-time hazard, unobserved heterogeneity, frailty File-URL: http://www.stata-journal.com/article.html?article=st0805 File-Function: link to article purchase X-DOI: 10.1177/1536867X261477503 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-3/st0805 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:325-366 Template-Type: ReDIF-Article 1.0 Author-Name: Chirok Han Author-Workplace-Name: Korea University Author-Email: chirokhan@korea.ac.kr Author-Person: pha335 Author-Name: Goeun Lee Author-Workplace-Name: Kookmin University Author-Email: goeunlee@kookmin.ac.kr Author-Person: ple1098 Title: Bias correction for the within-group estimator for panel-data sample-selection models Journal: Stata Journal Pages: 367-397 Issue: 3 Volume: 26 Year: 2026 Month: September Abstract: Han and Lee (2022, Economics Letters 220: art. 110882) propose a least-squares approach to correct for selectivity bias in the within-group estimator of linear panel-data models with fixed effects and sample selection. In this article, we review that estimator and introduce a command that provides a numerically stable implementation. Keywords: xtselfe, panel data, fixed effects, sample selection, within-group File-URL: http://www.stata-journal.com/article.html?article=st0806 File-Function: link to article purchase X-DOI: 10.1177/1536867X261477565 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-3/st0806 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:367-397 Template-Type: ReDIF-Article 1.0 Author-Name: Ian R. White Author-Workplace-Name: UCL Innovative Clinical Trials Unit Author-Email: ian.white@ucl.ac.uk Author-Person: pwh62 Author-Name: Ella Marley-Zagar Author-Workplace-Name: MRC Clinical Trials Unit at UCL Author-Email: ellamarleyzagar@icloud.com Author-Name: Tim P. Morris Author-Workplace-Name: MRC Clinical Trials Unit at UCL Author-Email: tim.morris@novartis.com Title: siman: Explore, visualize, and analyze the output of simulation studies Journal: Stata Journal Pages: 398-429 Issue: 3 Volume: 26 Year: 2026 Month: September Abstract: 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 com- plex, 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 pro- grams that offers data manipulation, exploration, visualization, and analysis of the results of complex simulation studies. Keywords: siman, siman analyse, siman blandaltman, siman compare- methodsscatter, siman describe, siman import, siman lollyplot, siman nestloop, siman scatter, siman setup, siman swarm, siman table, siman zipplot, nestloop, simsum, statistical methodology, simulation study, Monte Carlo error, ADEMP, zipplot, lollyplot, nested loop plot File-URL: http://www.stata-journal.com/article.html?article=st0807 File-Function: link to article purchase X-DOI: 10.1177/1536867X261477504 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-3/st0807 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:398-429 Template-Type: ReDIF-Article 1.0 Author-Name: Woosik Gong Author-Workplace-Name: University of Wisconsin at Madison Author-Email: wgong28@wisc.edu Author-Name: Gregory F. Cox Author-Workplace-Name: National University of Singapore Author-Email: ecsgfc@nus.edu.sg Author-Name: Xiaoxia Shi Author-Workplace-Name: University of Wisconsin at Madison Author-Email: xshi@ssc.wisc.edu Author-Person: psh410 Title: Commands for full-vector and subvector inference in moment-inequality models Journal: Stata Journal Pages: 430-460 Issue: 3 Volume: 26 Year: 2026 Month: September Abstract: 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 eco- nomics 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 hypothe- sis test for the compatibility of a parameter value with the model. The ccscc com- mand 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. Keywords: ccscc, sccintreg, moment-inequality models, conditional χ2 test File-URL: http://www.stata-journal.com/article.html?article=st0808 File-Function: link to article purchase X-DOI: 10.1177/1536867X261477598 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-3/st0808 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:430-460 Template-Type: ReDIF-Article 1.0 Author-Name: Jan Kemper Author-Workplace-Name: University of Mannheim Author-Email: jan.kemper@zew.de Author-Person: pke417 Author-Name: Davud Rostam-Afschar Author-Workplace-Name: University of Mannheim Author-Email: rostam-afschar@uni-mannheim.de Author-Person: pro386 Title: Earning while learning: How to run batched bandit experiments Journal: Stata Journal Pages: 461-493 Issue: 3 Volume: 26 Year: 2026 Month: September Abstract: Researchers typically collect experimental data sequentially, allowing early outcome observations and adaptive treatment assignment to reduce expo- sure to inferior treatments. In this article, we review multiarmed bandit adaptive experimental designs that balance exploration and exploitation. Because adap- tively collected experimental data through bandit algorithms violate standard asymptotics, inference is challenging. We implement an estimator that yields valid heteroskedasticity-robust confidence intervals in batched bandit designs and compare coverage in Monte Carlo simulations. We introduce bbandits for Stata, a community-contributed package for designing experiments via simulation, run- ning interactive bandit experiments, and implementing and analyzing adaptively collected data. bbandits includes three common assignment algorithms—ε-first, ε-greedy, and Thompson sampling—and supports estimation, inference, and visu- alization. Keywords: bbandits, randomized controlled trial, causal inference, multi-armed bandits, experimental design, machine learning File-URL: http://www.stata-journal.com/article.html?article=st0809 File-Function: link to article purchase X-DOI: 10.1177/1536867X261477558 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-3/st0809 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:461-493 Template-Type: ReDIF-Article 1.0 Author-Name: Nicholas J. Cox Author-Workplace-Name: Durham University Author-Email: n.j.cox@durham.ac.uk Author-Person: pco34 Title: Speaking Stata: Quantile-box plots and beyond: Variations on a theme by Emanuel Parzen Journal: Stata Journal Pages: 494-529 Issue: 3 Volume: 26 Year: 2026 Month: September Abstract: 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 distri- butions 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 in- formation 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 con- venient 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. Keywords: quantile-box plot, quantile plot, box plot, quantiles, quartiles, medians, means, geometric means, logarithmic scale, transformations, confidence intervals, distributions, graphics File-URL: http://www.stata-journal.com/article.html?article=gr0104 File-Function: link to article purchase X-DOI: 10.1177/1536867X261477597 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-3/gr0104 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:494-529 Template-Type: ReDIF-Article 1.0 Author-Name: Demetris Christodoulou Author-Workplace-Name: The University of Sydney Author-Email: Demetris.Christodoulou@sydney.edu.au Author-Person: pch1698 Title: Stata tip 168: ASCII bytes and Unicode code points Journal: Stata Journal Pages: 530-536 Issue: 3 Volume: 26 Year: 2026 Month: September File-URL: http://www.stata-journal.com/article.html?article=pr0085 File-Function: link to article purchase X-DOI: 10.1177/1536867X261477487 Handle:RePEc:tsj:stataj:v:25:y:2025:i:3:p:530-536 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 537 Issue: 3 Volume: 26 Year: 2026 Month: September Abstract: Updates for previously published packages are provided. Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-3/dm0085_4/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-3/gr41_6/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-3/gr42_10/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-3/st0360_1/ Handle:RePEc:tsj:stataj:v:26:y:2026:i:3:p:537