Template-Type: ReDIF-Article 1.0 Author-Name: Giovanbattista Califano Author-Workplace-Name: University of Naples Federico II Author-Email: giovanbattista.califano@unina.it Author-Person: pca1616 Author-Name: Rosa Fabbricatore Author-Workplace-Name: University of Naples Federico II Author-Email: rosa.fabbricatore@unina.it Title: Relating latent class membership to covariates and outcomes: Two bias-adjusted methods in Stata Journal: Stata Journal Pages: 153-176 Issue: 2 Volume: 26 Year: 2026 Month: June Abstract: Finite mixture models are versatile tools for modeling unobserved population heterogeneity because they identify latent subgroups within a population from a set of observed variables. A common extension involves linking these classes to covariates or outcomes for further analysis in a stepwise fashion. However, standard methods for this task can introduce bias due to misclassification error when assigning observations to a latent class. In this article, we introduce the step3 command, which implements two bias-adjusted methods—the Bolck–Croon–Hagenaars method and the maximum likelihood approach—that address these issues by accounting for classification uncertainty. We explain the nature of the biases in standard approaches, present the theoretical foundations of these bias-adjusted methods, and provide practical implementation details using step3. Through a simulation study, we illustrate the advantages of these methods in reducing bias and improving estimation accuracy. Keywords: step3, finite mixture models, latent class, auxiliary variables File-URL: http://www.stata-journal.com/article.html?article=st0801 File-Function: link to article purchase DOI: 10.1177/1536867X261449931 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-2/st0774/ Handle: RePEc:tsj:stataj:v:26:y:2026:i:2:p:153-176 Template-Type: ReDIF-Article 1.0 Author-Name: Sascha O. Becker Author-Workplace-Name: University of Warwick Author-Email: s.o.becker@warwick.ac.uk Author-Person: pbe98 Author-Name: P. David Boll Author-Workplace-Name: University of Warwick Author-Email: david.boll@warwick.ac.uk Author-Person: pbo1237 Author-Name: Hans-Joachim Voth Author-Workplace-Name: University of Zurich Author-Email: voth@econ.uzh.ch Author-Person: pvo5 Title: Testing and correcting for spatial unit roots in regression analysis Journal: Stata Journal Pages: 177-202 Issue: 2 Volume: 26 Year: 2026 Month: June Abstract: Spatial unit roots can lead to spurious regression results. We present an overview of the methods developed in Müller and Watson (2024, Econometrica 92: 1661–1695) to test and correct for spatial unit roots and introduce a suite of commands (spur) implementing these techniques. Our commands exactly replicate results in Müller and Watson (2024) using the same data as Chetty et al. (2014, Quarterly Journal of Economics 129: 1553–1623). As a guide for applied researchers, we provide a practical algorithm for regression analysis using these methods and a simulated illustration in Stata. Keywords: spur, spurtest, spurtransform, spurhalflife, spurious spatial regression, spatial unit roots File-URL: http://www.stata-journal.com/article.html?article=st0802 File-Function: link to article purchase DOI: 10.1177/1536867X261449932 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-2/st0802/ Handle: RePEc:tsj:stataj:v:26:y:2026:i:2:p:177-202 Template-Type: ReDIF-Article 1.0 Author-Name: Luca Fumarco Author-Workplace-Name: Masaryk University Author-Email: luca.fumarco@econ.muni.cz Author-Person: pfu155 Author-Name: Jaroslav Groero Author-Workplace-Name: Center for Economic Research and Graduate Education—Economics Institute Author-Email: jaroslav.groero@cerge-ei.cz Author-Person: pgr785 Title: onlyuseful: A package that automagically keeps only variables used in the do-file Journal: Stata Journal Pages: 203-212 Issue: 2 Volume: 26 Year: 2026 Month: June Abstract: onlyuseful is a command that automates dataset reduction by retain- ing only the variables explicitly used in a Stata script. By leveraging PowerShell, it enhances reproducibility and efficiency in data management for large datasets and supports research replicability. Keywords: onlyuseful, PowerShell, replicability File-URL: http://www.stata-journal.com/article.html?article=dm0117 File-Function: link to article purchase DOI: 10.1177/1536867X261449933 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-2/dm0117/ Handle: RePEc:tsj:stataj:v:26:y:2026:i:2:p:203-212 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: Three commands for data reduction Journal: Stata Journal Pages: 291-322 Issue: 2 Volume: 26 Year: 2026 Month: June Abstract: Three new commands for data reduction—cisets, pctilesets, and quantilesets—are introduced with examples. Each command lists a set of results and saves those results to a separate dataset, whether of confidence intervals or of percentiles or quantiles. Immediate applications include graphical displays, illustrated by examples of confidence intervals shown in various ways and of complements and alternatives to box plots. Keywords: cisets, pctilesets, quantilesets, confidence intervals, percentiles, quantiles, quantile plots, box plots, data reduction File-URL: http://www.stata-journal.com/article.html?article=dm0119 File-Function: link to article purchase DOI: 10.1177/1536867X261450274 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-2/dm0119/ Handle: RePEc:tsj:stataj:v:26:y:2026:i:2:p:291-322 Template-Type: ReDIF-Article 1.0 Author-Name: Kerui Du Author-Workplace-Name: Xiamen University Author-Email: kerrydu@xmu.edu.cn Author-Name: Chunxia Chen Author-Workplace-Name: Xiamen University Author-Email: chenchunxia@stu.xmu.edu.cn Author-Name: Yang Song Author-Workplace-Name: Hefei University of Technology Author-Email: ss0706082021@163.com Author-Name: Shuo Hu Author-Workplace-Name: Southwestern University of Finance and Economics Author-Email: advancehs@163.com Author-Name: Ruipeng Tan Author-Workplace-Name: Hefei University of Technology Author-Email: tanruipeng@hfut.edu.cn Title: Reading and processing geographical raster data in Stata Journal: Stata Journal Pages: 213-243 Issue: 2 Volume: 26 Year: 2026 Month: June Abstract: We integrate the Java GeoTools and NetCDF libraries into Stata and introduce a new suite of commands for reading and processing geospatial raster data entirely within Stata. These commands enable Stata users to seamlessly extract data from GeoTIFF and NetCDF files, reproject geographic coordi- nates, match raster data with geographical locations, and compute zonal statistics. By eliminating round trips to external GIS or scripting environments, the commands streamline workflows and improve reproducibility of spatial analyses conducted in Stata. We document the command syntax and provide worked examples that illustrate typical use cases—metadata inspection, subsetting, coordinate reference systems harmonization, polygon-based aggregation, and point-based exposure estimation—demonstrating how the package supports end-to-end spatial data preparation and analysis. Keywords: readraster, gtiffdisp, ncdisp, gtiffread, ncread, zonalstats, crsconvert, matchgeop, geotools_init, netcdf_init, GeoTIFF, NetCDF, Java, raster, geospatial File-URL: http://www.stata-journal.com/article.html?article=dm0118 File-Function: link to article purchase DOI: 10.1177/1536867X261449934 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-2/dm0118/ Handle: RePEc:tsj:stataj:v:26:y:2026:i:2:p:213-243 Template-Type: ReDIF-Article 1.0 Author-Name: Pinshan Pan Author-Workplace-Name: Boston College Author-Email: pinshan.pan@bc.edu Author-Name: Heejoon Han Author-Workplace-Name: Sungkyunkwan University Author-Email: heejoonhan@skku.edu Author-Person: pha400 Author-Name: Gyure Kim Author-Workplace-Name: Sungkyunkwan University Author-Email: kimkyokkr77@g.skku.edu Title: The cross-quantilogram: Measuring quantile dependence and testing directional predictability across time-series and cross-sectional data Journal: Stata Journal Pages: 244-273 Issue: 2 Volume: 26 Year: 2026 Month: June Abstract: In this article, we introduce the package crossq, a user-friendly tool for estimating and visualizing the cross-quantilogram, which is a method that captures quantile dependence between two series and tests for directional pre- dictability. The package includes three core commands: crossq_main estimates the cross-quantilogram coefficients using unconditional or conditional approaches; crossq_qstat performs quantile-based directional predictability tests using Box–Pierce and Ljung–Box-type Q statistics; and crossq_plot visualizes the results with confidence intervals or heatmaps across quantile combinations. Additional features include the partial cross-quantilogram, stationary bootstrap inference, and flexible customization options for estimation, testing, and visualization. These make the package suitable for a wide range of empirical applications in both time-series and cross-sectional contexts. Keywords: crossq, crossq_main, crossq_qstat, crossq_plot, cross-quan- tilogram, quantile dependence, directional predictability, stationary bootstrap File-URL: http://www.stata-journal.com/article.html?article=st0803 File-Function: link to article purchase DOI: 10.1177/1536867X261449935 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-2/st0803/ Handle: RePEc:tsj:stataj:v:26:y:2026:i:2:p:244-273 Template-Type: ReDIF-Article 1.0 Author-Name: Natalie Daya Malek Author-Workplace-Name: Bloomberg School of Public Health, Johns Hopkins University Author-Email: ndaya1@jhu.edu Author-Name: Dan Wang Author-Workplace-Name: Bloomberg School of Public Health, Johns Hopkins University Author-Email: dwang77@jhu.edu Author-Name: Sui Zhang Author-Workplace-Name: Bloomberg School of Public Health, Johns Hopkins University Author-Email: szhan137@jhu.edu Author-Name: Michael Fang Author-Workplace-Name: Bloomberg School of Public Health, Johns Hopkins University Author-Email: mfang9@jhu.edu Author-Name: Amelia Wallace Author-Workplace-Name: Bloomberg School of Public Health, Johns Hopkins University Author-Email: awallace@jhu.edu Author-Name: Scott Zeger Author-Workplace-Name: Bloomberg School of Public Health, Johns Hopkins University Author-Email: sz@jhu.edu Author-Name: Elizabeth Selvin Author-Workplace-Name: Bloomberg School of Public Health, Johns Hopkins University Author-Email: eselvin@jhu.edu Title: Summarizing data from continuous glucose monitors using the cgmstats command Journal: Stata Journal Pages: 274-290 Issue: 2 Volume: 26 Year: 2026 Month: June Abstract: In this article, we present the cgmstats command, which analyzes continuous glucose monitoring (CGM) data. The use of wearable CGMs is growing rapidly. The latest generation of CGM systems does not require fingerstick calibration, is minimally invasive, and is frequently used in research studies. CGM sensors are typically worn for up to 2 weeks and record interstitial glucose measurements every minute to every 15 minutes, depending on the sensor used. CGM systems generate hundreds of measurements per day and thousands of measurements in one person over a single wear. There is a need for tools that allow researchers to efficiently organize and summarize the wealth of data on glucose patterns produced by CGM systems. The cgmstats command generates CGM summary measures for data from various CGM systems and allows the user to flexibly define ranges and generate data visualizations. In this article, we provide an overview of the cgmstats command and examples of its use. The cgmstats command supports rigorous and reproducible analyses of CGM data. Keywords: cgmstats, continuous glucose monitoring metrics, glucose profile File-URL: http://www.stata-journal.com/article.html?article=st0804 File-Function: link to article purchase DOI: 10.1177/1536867X261450271 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-2/st0804/ Handle: RePEc:tsj:stataj:v:26:y:2026:i:2:p:274-290 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 323 Issue: 2 Volume: 26 Year: 2026 Month: June Abstract: Updates for previously published packages are provided. Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-2/gr0053_2/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-2/gr0095_1/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-2/st0528_2/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj26-2/st0678_1/ Handle:RePEc:tsj:stataj:v:26:y:2026:i:2:p:323