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
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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
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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
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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
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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
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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
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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
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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
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DOI: 10.1177/1536867X241297951
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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
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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
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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
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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
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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.
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