Template-Type: ReDIF-Article 1.0 Author-Name: Nicholas J. Cox Author-Workplace-Name: Durham University Author-Person: pco34 Author-Name: Stephen P. Jenkins Author-Workplace-Name: London School of Economics Author-Email: s.jenkins@lse.ac.uk Author-Person: pje7 Title: The Stata Journal Editors’ Prize 2024: Ian R. White Journal: Stata Journal Pages: 553-556 Issue: 4 Volume: 24 Year: 2024 Month: December DOI: 10.1177/1536867X241297909 File-URL: http://www.stata-journal.com/article.html?article=gn0101 File-Function: link to article purchase Handle:RePEc:tsj:stataj:v:24:y:2024:i:4:p:553-556 Template-Type: ReDIF-Article 1.0 Author-Name: Christopher F Baum Author-Workplace-Name: Boston College Author-Email: baum@bc.edu Author-Person: pba1 Author-Name: Denni Tommasi Author-Workplace-Name: University of Bologna Author-Email: denni.tommasi@unibo.it Author-Person: pto487 Author-Name: Lina Zhang Author-Workplace-Name: University of Amsterdam Author-Email: l.zhang5@uva.nl Author-Person: pzh886 Title: Estimating treatment effects when program participation is misreported Journal: Stata Journal Pages: 614-629 Issue: 4 Volume: 24 Year: 2024 Month: December DOI: 10.1177/1536867X241297916 Abstract: Instrumental variables are commonly used to estimate treatment effects in cases of imperfect compliance. However, if participation in the program is misreported, standard techniques can yield severely biased results. We present a new command, ivreg2m, that implements the mismeasured robust local average treatment-effect estimator developed by Calvi, Lewbel, and Tommasi (2022, Jour- nal of Business and Economic Statistics 40: 1701–1717) and Tommasi and Zhang (2024b, Journal of Applied Econometrics, https://doi.org/10.1002/jae.3079), to estimate the heterogeneous treatment effect of a program in the presence of treat- ment noncompliance and misreporting. The ivreg2m command can be used as the preferred strategy in cases of exogenous (nondifferential) misclassification. Keywords: heterogeneous treatment effects, LATE, misreporting, instrumental variables File-URL: http://www.stata-journal.com/article.html?article=st0758 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-4/st0758/ Handle:RePEc:tsj:stataj:v:24:y:2024:i:4:p:614-629 Template-Type: ReDIF-Article 1.0 Author-Name: Federico Belotti Author-Workplace-Name: Tor Vergata University of Rome Author-Email: federico.belotti@uniroma2.it Author-Person: pbe427 Author-Name: Giulia Mancini Author-Workplace-Name: University of Sassari Author-Email: gmancini@uniss.it Author-Person: pma2584 Author-Name: Giovanni Vecchi Author-Workplace-Name: Tor Vergata University of Rome Author-Email: giovanni.vecchi@uniroma2.it Author-Person: pve37 Title: Outlier detection for inequality and poverty analysis Journal: Stata Journal Pages: 630-665 Issue: 4 Volume: 24 Year: 2024 Month: December DOI: 10.1177/1536867X241297918 Abstract: Extreme values are common in survey data and represent a recurring threat to the reliability of both poverty and inequality estimates. The adoption of a consistent criterion for outlier detection is useful in many practical applications, particularly when international and intertemporal comparisons are involved. In this article, we discuss a simple univariate detection procedure to flag outliers. We present outdetect, a command that implements the procedure and provides useful diagnostic tools. The output of outdetect compares statistics obtained before and after the exclusion of outliers, with a focus on inequality and poverty measures. Finally, we carry out an extensive sensitivity exercise where the same outlier detection method is applied consistently to per capita expenditure across more than 30 household budget surveys. The results are clear and provide a sense of the influence of extreme values on poverty and inequality estimates. Keywords: outdetect, outliers, extreme values, inequality, poverty, incremental trimming curve File-URL: http://www.stata-journal.com/article.html?article=st0759 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-4/st0759/ Handle:RePEc:tsj:stataj:v:24:y:2024:i:4:p:630-665 Template-Type: ReDIF-Article 1.0 Author-Name: Alyssa Carlson Author-Workplace-Name: University of Missouri Author-Email: carlsonah@missouri.edu Author-Person: pca1490 Title: gtsheckman: Generalized two-step Heckman estimator Journal: Stata Journal Pages: 687-710 Issue: 4 Volume: 24 Year: 2024 Month: December DOI: 10.1177/1536867X241297921 Abstract: In this article, I introduce the gtsheckman command, which estimates a generalized two-step Heckman sample-selection estimator adjusted for heteroskedasticity. This estimator has been previously proposed in Carlson and Joshi (2024, Journal of Applied Econometrics 39: 237–255), where the presence of heteroskedasticity was motivated by a panel-data setting with random coefficients. The gtsheckman command offers several advantages over the heckman, twostep command, including robust inference, a more general control function specification, and the incorporation of heteroskedasticity. I discuss syntax and the underlying methodology and provide examples showing how the gtsheckman command can be used in a variety of settings. Keywords: gtsheckman, heckman, sample selection, heteroskedasticity File-URL: http://www.stata-journal.com/article.html?article=st0761 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-4/st0761/ Handle:RePEc:tsj:stataj:v:24:y:2024:i:4:p:687-710 Template-Type: ReDIF-Article 1.0 Author-Name: Damian Clarke Author-WorkPlace-Name: University of Exeter Author-Email: dclarke@fen.uchile.cl Author-Person: pcl102 Author-Name: Daniel Pailañir Author-WorkPlace-Name: University of Chile Author-Email: dpailanir@fen.uchile.cl Author-Name: Susan Athey Author-WorkPlace-Name: Stanford University Author-Email: athey@stanford.edu Author-Person: pat6 Author-Name: Guido Imbens Author-WorkPlace-Name: Stanford University Author-Email: imbens@stanford.edu Author-Person: pim4 Title: On synthetic difference-in-differences and related estimation methods in Stata Journal: Stata Journal Pages: 557-598 Issue: 4 Volume: 24 Year: 2024 Month: December DOI: 10.1177/1536867X241297914 Abstract: In this article, we describe a computational implementation of the synthetic difference-in-differences (SDID) estimator of Arkhangelsky et al. (2021, American Economic Review 111: 4088–4118) for Stata. SDID can be used in many circumstances where treatment effects on some particular policy or event are desired and repeated observations on treated and untreated units are available over time. We lay out the theory underlying SDID both when there is a single treatment adoption date and when adoption is staggered over time, and we discuss estimation and inference in each of these cases. We introduce the sdid command, which implements these methods in Stata, and provide several examples of use, discussing estimation, inference, and visualization of results. Along with SDID, the sdid command allows for the implementation of standard synthetic control and difference-in-differences methods in an identical framework, permitting estimation, inference, and the generation of graphical output in a computationally efficient way. Keywords: synthetic difference in differences, synthetic control, difference in differences, estimation, inference, visualization File-URL: http://www.stata-journal.com/article.html?article=st0757 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-4/st0757/ Handle:RePEc:tsj:stataj:v:24:y:2024:i:4:p:557-598 Template-Type: ReDIF-Article 1.0 Author-Name: Sergio Correia Author-Workplace-Name: Board of Governors of the Federal Reserve System Author-Email: sergio.a.correia@frb.gov Author-Person: pco826 Author-Name: Matthew P. Seay Author-Workplace-Name: Board of Governors of the Federal Reserve System Author-Email: matt.seay@frb.gov Title: require: Package dependencies for reproducible research Journal: Stata Journal Pages: 599-613 Issue: 4 Volume: 24 Year: 2024 Month: December DOI: 10.1177/1536867X241297915 Abstract: The ability to conduct reproducible research in Stata is often limited by the lack of version control for community-contributed packages. In this article, we introduce the require command, a tool designed to ensure package dependen- cies are compatible across users and computer systems. Given a list of packages, require verifies that each package is installed, checks for a minimum or exact version or package release date, and optionally installs the package if prompted by the researcher. Keywords: require, SSC, which, reproducible research, package management, dependency management, GitHub File-URL: http://www.stata-journal.com/article.html?article=pr0081 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-1/pr0081/ Handle:RePEc:tsj:stataj:v:24:y:2024:i:4:p:599-613 Template-Type: ReDIF-Article 1.0 Author-Name: Malick Dione Author-Email: malick.dione@cgiar.org Author-WorkPlace-Name: International Food Policy Research Institute Author-Name: Greg Seymour Author-Email: gregory.t.seymour@census.gov Author-WorkPlace-Name: United States Census Bureau Author-Person: pse689 Author-Name: Nathaniel Ferguson Author-Email: n.ferguson@cgiar.org Author-WorkPlace-Name: International Food Policy Research Institute Author-Name: Hazel Malapit Author-Email: h.malapit@cgiar.org Author-WorkPlace-Name: International Food Policy Research Institute Author-Person: pma2598 Title: Calculating the Women’s Empowerment in Agriculture Index (WEAI) using Stata Journal: Stata Journal Pages: 746-765 Issue: 4 Volume: 24 Year: 2024 Month: December DOI: 10.1177/1536867X241297923 Abstract: The Women’s Empowerment in Agriculture Index (WEAI) is a standardized, survey-based tool that has been widely used to track gender equality and measure empowerment, agency, and women’s inclusion in the agricultural sector (Alkire et al., 2013, World Development 52: 71–91). Since the WEAI’s release in 2012, an abbreviated version of the WEAI (A-WEAI) and a project-level version of the WEAI (pro-WEAI) have been developed (Malapit et al., 2017, The abbrevi- ated Women’s Empowerment in Agriculture Index [A-WEAI]; Malapit et al., 2019, Development of the project-level Women’s Empowerment in Agriculture Index [pro-WEAI]). In this article, we review the shared methodology that underlies all members of the WEAI family of indices and introduce a command, weai, that can be used for index calculation. We describe the command and its options and provide empirical examples using publicly available data from the Gender, Agriculture, and Assets Project, Phase 2. Keywords: WEAI, empowerment, agency File-URL: http://www.stata-journal.com/article.html?article=st0763 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-4/st0763/ Handle:RePEc:tsj:stataj:v:24:y:2024:i:4:p:746-765 Template-Type: ReDIF-Article 1.0 Author-Name: Stephen Nash Author-Workplace-Name: London School of Hygiene and Tropical Medicine Author-Email: stevienashaa@gmail.com Author-Name: Katy E. Morgan Author-Workplace-Name: London School of Hygiene and Tropical Medicine Author-Email: katy.morgan@lshtm.ac.uk Author-Name: Chris Frost Author-Workplace-Name: London School of Hygiene and Tropical Medicine Author-Email: chris.frost@lshtm.ac.uk Author-Name: Amy Mulick Author-Workplace-Name: London School of Hygiene and Tropical Medicine Author-Email: amy.mulick@lshtm.ac.uk Title: Erratum: Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command Journal: Stata Journal Pages: 784-787 Issue: 4 Volume: 24 Year: 2024 Month: September Abstract: Trials of interventions that aim to slow disease progression may analyze a continuous outcome by comparing its change over time—its slope—between the treated and the untreated group using a linear mixed model. To perform a sample-size calculation for such a trial, one must have estimates of the parameters that govern the between- and within-subject variability in the outcome, which are often unknown. The algebra needed for the sample-size calculation can also be complex for such trial designs. We have written a new user-friendly command, slopepower, that performs sample-size or power calculations for trials that com- pare slope outcomes. The package is based on linear mixed-model methodology, described for this setting by Frost, Kenward, and Fox (2008, Statistics in Medicine 27: 3717–3731). In the first stage of this approach, slopepower obtains estimates of mean slopes together with variances and covariances from a linear mixed model fit to previously collected user-supplied data. In the second stage, these estimates are combined with user input about the target effectiveness of the treatment and design of the future trial to give an estimate of either a sample size or a statistical power. In this article, we present the slopepower command, briefly explain the methodology behind it, and demonstrate how it can be used to help plan a trial and compare the sample sizes needed for different trial designs. Keywords: slopepower, power, sample-size calculations, slopes, parallel-arm trial File-URL: http://www.stata-journal.com/article.html?article=st0647_1 File-Function: link to article purchase DOI: 10.1177/1536867X241297951 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-3/st0647_1/ Handle:RePEc:tsj:stataj:v:24:y:2024:i:4:p:784-787 Template-Type: ReDIF-Article 1.0 Author-Name: Andrew Pickles Author-Email: andrew.pickles@kcl.ac.uk Author-WorkPlace-Name: King’s College London Author-Name: Matt Bluett-Duncan Author-Email: matthew.bluett-duncan@manchester.ac.uk Author-WorkPlace-Name: University of Manchester Author-Name: Helen Sharp Author-Email: hmsharp@liverpool.ac.uk Author-WorkPlace-Name: University of Liverpool Author-Name: Silia Vitoratou Author-Email: silia.vitoratou@kcl.ac.uk Author-WorkPlace-Name: King’s College London Title: Distinguishing differences in construct from differences in response style: gsem for item response theory models with anchoring vignettes Journal: Stata Journal Pages: 666-686 Issue: 4 Volume: 24 Year: 2024 Month: December DOI: 10.1177/1536867X241297920 Abstract: Item response theory models allow estimation of participant and group-mean trait scores from responses to a set of items, but estimates can be biased when participants vary in their response style. We illustrate models fit in gsem that can account for such response style differences by comparing self-report with their ratings of anchoring vignettes—descriptions of other individuals displaying different levels of the trait. Simulation results from standard item response the- ory, mean bias, random bias, and free-threshold models are illustrated. We show that unbiased estimates can be recovered when the vignettes rated depend on the participants’ own self-rating or are even rated by a different sample, substantially broadening their scope of application. Keywords: item response theory, IRT, anchoring vignettes, response bias, cross-culture calibration File-URL: http://www.stata-journal.com/article.html?article=st0760 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-4/st0760/ Handle:RePEc:tsj:stataj:v:24:y:2024:i:4:p:666-686 Template-Type: ReDIF-Article 1.0 Author-Name: Daniele Spinelli Author-WorkPlace-Name: University of Milano–Bicocca Author-Email: daniele.spinelli@unimib.it Author-Person: psp173 Author-Name: Salvatore Ingrassia Author-WorkPlace-Name: University of Catania Author-Email: salvatore.ingrassia@unict.it Author-Person: pin67 Author-Name: Giorgio Vittadini Author-WorkPlace-Name: University of Milano–Bicocca Author-Email: giorgio.vittadini@unimib.it Author-Person: pvi274 Title: Cluster-weighted models using Stata Journal: Stata Journal Pages: 711-745 Issue: 4 Volume: 24 Year: 2024 Month: December DOI: 10.1177/1536867X241297922 Abstract: The cluster-weighted model (CWM) is a member of the family of mixtures of regression models and is also known as a mixture of regressions with random covariates. CWMs refer to the framework of model-based clustering and naturally apply when the research interest requires modeling the relationship be- tween a response variable and a set of covariates using a regression-based approach such as a generalized linear model with the sample being suspected of compris- ing heterogeneous latent classes. A command for fitting these models is not yet available in Stata, so the aim of this article is to introduce the package cwmglm, which fits CWMs based on the most common generalized linear models with ran- dom covariates. Moreover, cwmglm allows the estimation of parsimonious models of Gaussian distributions, with the parameterization of the variance–covariance matrix based on the eigenvalue decomposition. These features are completely new for Stata users. The cwmglm package features goodness-of-fit, bootstrapping, and model-selection tools. We illustrate the use of cwmglm with real and simulated datasets. Keywords: cluster-weighted model, finite mixtures of regressions with random covariates, model-based clustering, saturated mixture regression model, Gaussian parsimonious models, postestimation File-URL: http://www.stata-journal.com/article.html?article=st0762 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-4/st0762/ Handle:RePEc:tsj:stataj:v:24:y:2024:i:4:p:711-745 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: Getting by without the by() option: Some graphics for unequal groups Journal: Stata Journal Pages: 766-776 Issue: 4 Volume: 24 Year: 2024 Month: December DOI: 10.1177/1536867X231212453 Abstract: The by() option of the graph command is often used to show groups or subsets of some data in separate panels or facets of a graphical display. If groups are unequal in size, the result may seem awkward or inefficient in use of space. Devices to allow such groups to be shown directly without using a by() option are explained and exemplified for graph dot and its siblings and for graph twoway. New variables to show rank within groups and (if needed) separation of groups are easily constructed. Group summaries such as medians may easily be added. Graph types shown are dot charts, quantile plots, and displays using spikes to show differences between variables. Data examples are for ocean salinity and changes in weight of anorexic girls. Keywords: graphics, distributions, groups, dot charts, quantile plots, paired data, change, comparisons, by() option File-URL: http://www.stata-journal.com/article.html?article=gr0098 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-4/gr0092/ Handle:RePEc:tsj:stataj:v:24:y:2024:i:4:p:766-776 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: Stata tip 158: The devil is in the delta Journal: Stata Journal Pages: 777-783 Issue: 4 Volume: 24 Year: 2024 Month: December File-URL: http://www.stata-journal.com/article.html?article=st0764 File-Function: link to article purchase DOI: 10.1177/1536867X241297950 Handle:RePEc:tsj:stataj:v:24:y:2024:i:4:p:777-783 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 788-789 Issue: 4 Volume: 24 Year: 2024 Month: December DOI: 10.1177/1536867X241297954 Abstract: Updates for previously published packages are provided. Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-4/gr42_9/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-4/gr0025_1/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-4/gr0061_4/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-4/st0389_10/ Note: Windows users should not attempt to download these files with a web browser. Handle:RePEc:tsj:stataj:v:24:y:2024:i:4:p:788-789