Template-Type: ReDIF-Article 1.0 Author-Name: Priyanka Asnani Author-Email: Priyankaasnani13@gmail.com Author-Workplace-Name: Federal Reserve Bank of Dallas Author-Name: Alexander Chudik Author-Email: alexander.chudik@gmail.com Author-Workplace-Name: Federal Reserve Bank of Dallas Author-Person: pch972 Author-Name: Braden Strackman Author-Email: braden.strackman@dal.frb.org Author-Workplace-Name: Federal Reserve Bank of Dallas Title: xtpb: The pooled Bewley estimator of long-run relationships in dynamic heterogeneous panels Journal: Stata Journal Pages: 136-150 Issue: 1 Volume: 25 Year: 2025 Month: March X-DOI: 10.1177/1536867X251322965 Abstract: In this article, we introduce a new command, xtpb, that implements the Chudik, Pesaran, and Smith (Forthcoming, Econometrics and Statistics, https: //doi.org/10.1016/j.ecosta.2023.11.001) pooled Bewley (PB) estimator of long- run relationships in dynamic heterogeneous panel-data models. The PB estimator is based on the Bewley (1979, Economics Letters 3: 357–361) transform of the autoregressive-distributed lag model, and it is applicable under a similar setting to the widely used pooled mean group estimator of Pesaran, Shin, and Smith (1999, Journal of the American Statistical Association 94: 621–634). Two bias-correction methods and a bootstrapping algorithm for more accurate small-sample inference robust to arbitrary cross-sectional dependence of errors are also implemented. An empirical illustration reproduces the PB estimates of the consumption function as in Chudik, Pesaran, and Smith (Forthcoming). Keywords: tpb, pooled Bewley (PB) estimator, pooled mean group (PMG) estimator, heterogeneous dynamic panels, I(1) regressors, autoregressive-distribut- ed lag model, cross-sectional dependence, bias-correction, bootstrapping File-URL: http://hdl.handle.net/10.1177/1536867X251322965 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-1/st0773/ Handle:RePEc:tsj:stataj:v:25:y:2025:i:1:p:136-150 Template-Type: ReDIF-Article 1.0 Author-Name: Matteo Bottai Author-Email: matteo.bottai@ki.se Author-Workplace-Name: Karolinska Institutet Title: Maximum agreement regression with magreg Journal: Stata Journal Pages: 237-243 Issue: 1 Volume: 25 Year: 2025 Month: March X-DOI: 10.1177/1536867X251322972 Abstract: This note describes magreg, a command for estimating the coefficients of maximum-agreement regression models for an outcome variable given covariates. Recently introduced by Bottai et al. (2022, American Statistician 76: 313–321), maximum agreement regression maximizes the concordance correlation between the predicted values and the observed outcome values. The syntax of the command is nearly identical to that of regress, which estimates least-squares regression. This note shows the features of the command and its possible applications through a data example. Keywords: magreg, linear regression, Pearson’s correlation, Lin’s concordance correlation File-URL: http://hdl.handle.net/10.1177/1536867X251322972 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-1/st0773/ Handle:RePEc:tsj:stataj:v:25:y:2025:i:1:p:237-243 Template-Type: ReDIF-Article 1.0 Author-Name: Elisa Brini Author-Email: elisa.brini@unifi.it Author-Workplace-Name: University of Florence Author-Name: Solveig Topstad Borgen Author-Email: s.t.borgen@sosgeo.uio.no Author-Workplace-Name: University of Oslo Author-Name: Nicolai T. Borgen Author-Email: n.t.borgen@isp.uio.no Author-Workplace-Name: University of Oslo Title: Avoiding the eyeballing fallacy: Visualizing statistical differences between estimates using the pheatplot command Journal: Stata Journal Pages: 77-96 Issue: 1 Volume: 25 Year: 2025 Month: March X-DOI: 10.1177/1536867X251322962 Abstract: Graphical representations of coefficients and their confidence intervals are increasingly used in research presentations and publications because they are easier and quicker to read than tables. However, in coefficient plots that include several estimated coefficients, researchers often use confidence intervals to eyeball whether coefficients are statistically significant from each other, which results in an overly conservative test and increased risk of type II errors. To help avoid this eyeballing fallacy, we introduce the pheatplot postestimation command, which vi- sualizes the statistical significance across estimates of categorical variables in a re- gression model. pheatplot efficiently compares the significance level between point estimates and helps researchers avoid making wrong assumptions about whether estimates differ. Moreover, by representing p-values as continuous measures rather than binary thresholds, it provides the flexibility to move beyond arbitrary cut- offs of statistical significance. This article offers some examples that illustrate the functionality of the pheatplot command. Keywords: pheatplot, heatplot, lincom, coefplot, eyeballing fallacy, confi- dence intervals, p-values, statistical significance File-URL: http://hdl.handle.net/10.1177/1536867X251322962 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-1/gr0099/ Handle:RePEc:tsj:stataj:v:25:y:2025:i:1:p:77-96 Template-Type: ReDIF-Article 1.0 Author-Name: Niels Henrik Bruun Author-Email: nbru@rn.dk Author-Workplace-Name: Aalborg University Hospital Author-Person: pbr821 Author-Name: Nanna Maria Uldall Torp Author-Workplace-Name: Aalborg University Hospital Author-Name: Stine Linding Andersen Author-Workplace-Name: Aalborg University Hospital Title: Establishing reference interval bounds for censored and contaminated data Journal: Stata Journal Pages: 151-168 Issue: 1 Volume: 25 Year: 2025 Month: March X-DOI: 10.1177/1536867X251322968 Abstract: Reference intervals are essential across the medical and environmental fields. A reference interval (for example, the 95% central prediction interval) de- fines the normal range of measurements for a specific physiological parameter in healthy individuals. Inappropriate reference interval bounds may occur because of censored measurements (due to instrument limitations) or contaminated data (by accidentally sampling nonhealthy individuals). To address this, we propose using the regression-on-order-statistics (ROS) method combined with an optimal Box–Cox transformation. The ROS method involves regressing Gaussian scores based on ranks from ordered noncensored Box–Cox transformed measurements. To find the optimal Box–Cox transformation, we maximize the adjusted R2 when estimating the mean and standard deviation through regression of empirical Gaus- sian quantiles on measurements. We demonstrate how to identify contamination and introduce a new command, ros. Real-life data illustrate the effectiveness of the ROS method. Keywords: ros, reference interval bounds, censored data, regression-of-order-statistics method, Box–Cox transformation File-URL: http://hdl.handle.net/10.1177/1536867X251322968 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-1/st0769/ Handle:RePEc:tsj:stataj:v:25:y:2025:i:1:p:151-168 Template-Type: ReDIF-Article 1.0 Author-Name: Alyssa H. Carlson Author-Email: carlsonah@missouri.edu Author-Workplace-Name: University of Missouri Author-Person: pca1490 Author-Name: Wei Zhao Author-Email: Wei.Zhao@truist.com Author-Workplace-Name: Truist Bank Author-Person: pzh1114 Title: Heckman sample-selection estimators under heteroskedasticity Journal: Stata Journal Pages: 212-236 Issue: 1 Volume: 25 Year: 2025 Month: March X-DOI: 10.1177/1536867X251322971 Abstract: This article provides a practical guide for Stata users on the conse- quences of heteroskedasticity in sample-selection models. We review the properties of two Heckman sample-selection estimators, full-information maximum likelihood and limited-information maximum likelihood (LIML), under heteroskedasticity. In this case, full-information maximum likelihood is inconsistent, while LIML can be consistent in certain settings. For the LIML estimator under heteroskedasticity, we show that standard Stata commands are unable to produce correct standard errors and instead suggest the community-contributed command gtsheckman (Carlson, 2022, Statistical Software Components S459109, Department of Economics, Boston College; 2024, Stata Journal 24: 687–710). Because heteroskedasticity affects the performance of these two estimators, we also offer guidance on how to test for heteroskedasticity and the conditions needed for the LIML estimator to be consis- tent. The Monte Carlo simulations illustrate that the suggested testing procedures perform well in terms of appropriate size and power. Keywords: sample selection, heteroskedasticity, limited-information max- imum likelihood, LIML, full-information maximum likelihood, FIML, Breusch– Pagan test, Hausman test File-URL: http://hdl.handle.net/10.1177/1536867X251322971 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-1/st0772/ Handle:RePEc:tsj:stataj:v:25:y:2025:i:1:p:212-236 Template-Type: ReDIF-Article 1.0 Author-Name: Matias D. Cattaneo Author-Email: cattaneo@princeton.edu Author-Workplace-Name: Princeton University Author-Person: pca473 Author-Name: Richard K. Crump Author-Email: richard.crump@ny.frb.org Author-Workplace-Name: Federal Reserve Bank of New York Author-Person: pcr107 Author-Name: Max H. Farrell Author-Email: mhfarrell@gmail.com Author-Workplace-Name: University of California Santa Barbara Author-Person: pfa702 Author-Name: Yingjie Feng Author-Email: fengyj@sem.tsinghua.edu.cn Author-Workplace-Name: Tsinghua University Title: Binscatter regressions Journal: Stata Journal Pages: 3-50 Issue: 1 Volume: 25 Year: 2025 Month: March X-DOI: 10.1177/1536867X251322960 Abstract: In this article, we introduce the package binsreg, which implements the binscatter methods developed by Cattaneo et al. (2024a, arXiv:2407.15276 [stat.EM]; 2024b, American Economic Review 114: 1488–1514). The package com- prises seven commands: binsreg, binslogit, binsprobit, binsqreg, binstest, binspwc, and binsregselect. The first four commands implement binscatter plot- ting, point estimation, and uncertainty quantification (confidence intervals and confidence bands) for least-squares linear binscatter regression (binsreg) and for nonlinear binscatter regression (binslogit for logit regression, binsprobit for probit regression, and binsqreg for quantile regression). The next two commands focus on pointwise and uniform inference: binstest implements hypothesis test- ing procedures for parametric specifications and for nonparametric shape restric- tions of the unknown regression function, while binspwc implements multigroup pairwise statistical comparisons. The last command, binsregselect, implements data-driven number-of-bins selectors. The commands offer binned scatterplots and allow for covariate adjustment, weighting, clustering, and multisample anal- ysis, which is useful when studying treatment-effect heterogeneity in randomized and observational studies, among many other features. Keywords: binsreg, binslogit, binsprobit, binsqreg, binstest, binspwc, bin- sregselect, binscatter, binned scatterplot, nonparametrics, semiparametrics, parti- tioning estimators, B-splines, tuning parameter selection, confidence bands, shape and specification testing File-URL: http://hdl.handle.net/10.1177/1536867X241233672 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-1/st0765/ Handle:RePEc:tsj:stataj:v:25:y:2025:i:1:p:3-50 Template-Type: ReDIF-Article 1.0 Author-Name: Nicholas J. Cox Author-Email: n.j.cox@durham.ac.uk Author-Workplace-Name: Durham University Author-Person: pco34 Title: Stata tip 159: Absent friends: How to plot what is not present Journal: Stata Journal Pages: 244-251 Issue: 1 Volume: 25 Year: 2025 Month: March X-DOI: 10.1177/1536867X251322973 File-URL: http://hdl.handle.net/10.1177/1536867X251322973 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-1/gr0100/ Handle:RePEc:tsj:stataj:v:25:y:2025:i:1:p:244-251 Template-Type: ReDIF-Article 1.0 Author-Name: Kerui Du Author-Email: kerrydu@xmu.edu.cn Author-Workplace-Name: Xiamen University Author-Name: Federica Galli Author-Email: federica.galli14@unibo.it Author-Workplace-Name: University of Bologna Author-Name: Luojia Wang Author-Email: ljwang@stu.xmu.edu.cn Author-Workplace-Name: Xiamen University Title: A command to fit spatial stochastic frontier models with inefficiency spillovers Journal: Stata Journal Pages: 189-211 Issue: 1 Volume: 25 Year: 2025 Month: March X-DOI: 10.1177/1536867X251322970 Abstract: The interdependence among decision-making units challenges the as- sumption of cross-sectional independence in traditional stochastic frontier models. Based on the seminal spatial Durbin specification for the frontier function, Galli (2023, Journal of the Royal Statistical Society, C ser., 72: 346–367) introduced inefficiency spillovers to measure neighborhood effects related to the inefficiency determinants. This article presents a new command, sfsd, that fits the comprehen- sive spatial stochastic frontier model that Galli (2023) proposed, accommodating various spatial and nonspatial specifications in both the frontier and the ineffi- ciency equations. sfsd is the first command that includes different typologies of spatial spillovers in a stochastic frontier framework, facilitating the investigation of contemporary research topics such as agglomeration and technology diffusion at both the firm and the regional levels. The description, options, and illustrative examples for the command are outlined in this article. Keywords: sfsd, spatial stochastic frontier models, spillover effects, tech- nical efficiency, inefficiency spillovers File-URL: http://hdl.handle.net/10.1177/1536867X251322970 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-1/st0771/ Handle:RePEc:tsj:stataj:v:25:y:2025:i:1:p:189-211 Template-Type: ReDIF-Article 1.0 Author-Name: Simon Freyaldenhoven Author-Email: simon.freyaldenhoven@phil.frb.org Author-Workplace-Name: Federal Reserve Bank of Philadelphia Author-Person: pfr361 Author-Name: Christian B. Hansen Author-Email: chansen1@chicagobooth.edu Author-Workplace-Name: University of Chicago Author-Person: pha982 Author-Name: Jorge Pérez Pérez Author-Email: jorgepp@banxico.org.mx Author-Workplace-Name: Banco de México Author-Person: ppe453 Author-Name: Jesse M. Shapiro Author-Email: jesse_shapiro@fas.harvard.edu Author-Workplace-Name: Harvard University Author-Person: psh70 Author-Name: Constantino Carreto Author-Email: constantino.carreto@banxico.org.mx Author-Workplace-Name: Banco de México Title: xtevent: Estimation and visualization in the linear panel event-study designJournal: Stata Journal Pages: 97-135 Issue: 1 Volume: 25 Year: 2025 Month: March X-DOI: 10.1177/1536867X251322964 Abstract: Linear panel models and the “event-study plots” that often accompany them are popular tools for learning about policy effects. We introduce the xtevent package, which enables the construction of event-study plots following the suggestions in Freyaldenhoven et al. (Forthcoming, Visualization, identifica- tion, and estimation in the linear panel event-study design [Cambridge University Press]). The package implements various procedures to estimate the underlying policy effects and allows for nonbinary policy variables and estimation adjusting for preevent trends. Keywords: xtevent, xteventplot, xteventtest, get_unit_time_effects, lin- ear panel-data models, two-way fixed-effects regression, pretrends, event study File-URL: http://hdl.handle.net/10.1177/1536867X251322964 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-1/st0767/ Handle:RePEc:tsj:stataj:v:25:y:2025:i:1:p:97-135 Template-Type: ReDIF-Article 1.0 Author-Name: Daniel Klein Author-Email: klein@dzhw.eu Author-Workplace-Name: German Centre for Higher Education Research and Science Studies Author-Person: pkl25 Title: Stata tip 160: Drop capture program drop from ado-files Journal: Stata Journal Pages: 252-253 Issue: 1 Volume: 25 Year: 2025 Month: March X-DOI: 10.1177/1536867X251322974 File-URL: http://hdl.handle.net/10.1177/1536867X251322974 Handle:RePEc:tsj:stataj:v:25:y:2025:i:1:p:252-253 Template-Type: ReDIF-Article 1.0 Author-Name: Johannes König Author-Email: jkoenig@diw.de Author-Workplace-Name: DIW Berlin Author-Name: Christian Schluter Author-Email: christian.schluter@univ-amu.fr Author-Workplace-Name: Aix Marseille School of Economics Author-Person: psc680 Author-Name: Carsten Schröder Author-Email: cschroeder@diw.de Author-Workplace-Name: DIW Berlin Author-Person: psc151 Author-Name: Isabella Retter Author-Email: iretter@diw.de Author-Workplace-Name: DIW Berlin Author-Name: Mattis Beckmannshagen Author-Email: mbeckmannshagen@diw.de Author-Workplace-Name: DIW Berlin Title: The beyondpareto command for optimal extreme-value index estimation Journal: Stata Journal Pages: 169-188 Issue: 1 Volume: 25 Year: 2025 Month: March X-DOI: 10.1177/1536867X251322969 Abstract: In this article, we introduce the command beyondpareto, which estimates the extreme-value index for distributions that are Pareto-like, that is, whose upper tails are regularly varying and eventually become Pareto. The estimation is based on rank-size regressions, and the threshold value for the upper-order statis- tics included in the final regression is determined optimally by minimizing the asymptotic mean squared error. An essential diagnostic tool for evaluating the fit of the estimated extreme-value index is the Pareto quantile–quantile plot, pro- vided in the accompanying command pqqplot. The usefulness of our estimation approach is illustrated in several real-world examples focusing on the upper tail of German wealth and city-size distributions. Keywords: beyondpareto, pqqplot, rank-size regression, extreme value index, Pareto, Zipf’s law, heavy tails, bias File-URL: http://hdl.handle.net/10.1177/1536867X251322969 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-1/st0770/ Handle:RePEc:tsj:stataj:v:25:y:2025:i:1:p:169-188 Template-Type: ReDIF-Article 1.0 Author-Name: James B. McDonald Author-Email: James_McDonald@byu.edu Author-Workplace-Name: Brigham Young University Author-Person: pmc19 Author-Name: Jacob Triplett Author-Email: jacob_triplett@kenan-flagler.unc.edu Author-Workplace-Name: University of North Carolina Title: gintreg: Generalized interval regression Journal: Stata Journal Pages: 51-76 Issue: 1 Volume: 25 Year: 2025 Month: March X-DOI: 10.1177/1536867X251322961 Abstract: Many important research questions involve regression models in which the dependent variable is censored or reported in intervals rather than as a nu- merical value. A common approach to treating these problems is to assume that the data correspond to a certain distribution (for example, a normal distribution) and then apply maximum likelihood estimation. While this method is widely used in the literature, it can yield inconsistent estimators in the presence of either heteroskedasticity or distributional misspecification. The gintreg command is a partially adaptive maximum-likelihood estimation procedure that 1) generalizes the intreg command by relaxing the normality assumption and 2) draws from a library of flexible distributional forms. The treatment of heteroskedasticity is ex- panded to account for possible skewness and kurtosis. Additional options provide interaction with the estimation process, informative metrics, and visualizations. Right- and left-censored, interval, grouped, and point data can be accommodated with this method. Keywords: gintreg, gintregplot, interval regression, partially adaptive estimation, skewed generalized t, intreg, stintreg, gb2fit, gb2lfit, generalized beta of the second kind, heteroskedasticity File-URL: http://hdl.handle.net/10.1177/1536867X251322961 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-1/st766/ Handle:RePEc:tsj:stataj:v:25:y:2025:i:1:p:51-76 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 254 Issue: 1 Volume: 25 Year: 2025 Month: March X-DOI: 10.1177/1536867X251322975 Abstract: Updates for previously published packages are provided. Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj25-1/st0389_11/ Note: Windows users should not attempt to download these files with a web browser. Handle:RePEc:tsj:stataj:v:25:y:2025:i:1:p:254