Template-Type: ReDIF-Article 1.0 Author-Name: H. Joseph Newton Author-Workplace-Name: Texas A&M University Author-Name: Nicholas J. Cox Author-Workplace-Name: Durham University Author-Person: pco34 Title: The Stata Journal Editors’ Prize 2019: Matias D. Cattaneo Journal: Stata Journal Pages: 753-756 Issue: 4 Volume: 19 Year: 2019 Month: December File-URL: http://www.stata-journal.com/article.html?article=gn0081 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893613 Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:753-756 Template-Type: ReDIF-Article 1.0 Author-Name: Christopher F Baum Author-Workplace-Name: Boston College Author-Workplace-Name: DIW Berlin Author-Email: baum@bc.edu Author-Person: pba1 Author-Name: Arthur Lewbel Author-Workplace-Name: Boston College Author-Email: lewbel@bc.edu Author-Person: ple43 Title: Advice on using heteroskedasticity-based identification Journal: Stata Journal Pages: 757-767 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: Lewbel (2012, Journal of Business and Economic Statistics 30: 67–80) provides a heteroskedasticity-based estimator for linear regression models containing an endogenous regressor when no external instruments or other such information is available. The estimator is implemented in the command ivreg2h by Baum and Schaffer (2012, Statistical Software Components S457555, Department of Economics, Boston College). In this article, we give advice and instructions to researchers who want to use this estimator. Keywords: ivreg2h, instrumental variables, linear regression, endogeneity, identification, heteroskedasticity File-URL: http://www.stata-journal.com/article.html?article=st0575 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893614 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0575/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:757-767 Template-Type: ReDIF-Article 1.0 Author-Name: Victor Chernozhukov Author-Email: vchern@mit.edu Author-WorkPlace-Name: Massachusetts Institute of Technology Author-Person: pch864 Author-Name: Ivan Fernández-Val Author-Email: ivanf@bu.edu Author-WorkPlace-Name: Boston University Author-Person: pfe104 Author-Name: Sukjin Han Author-Email: sukjin.han@austin.utexas.edu Author-WorkPlace-Name: University of Texas at Austin Author-Person: pha802 Author-Name: Amanda Kowalski Author-Email: aekowals@umich.edu Author-WorkPlace-Name: University of Michigan Author-Person: pko425 Title: Censored quantile instrumental-variable estimation with Stata Journal: Stata Journal Pages: 768-781 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: Many applications involve a censored dependent variable, an endogenous independent variable, or both. Chernozhukov, Fernández-Val, and Kowalski (2015, Journal of Econometrics 186: 201–221) introduced a censored quantile instrumental-variable (CQIV) estimator for use in those applications. The estimator has been applied by Kowalski (2016, Journal of Business & Economic Statistics 34: 107–117), among others. In this article, we introduce a command, cqiv, that simplifies application of the CQIV estimator in Stata. We summarize the CQIV estimator and algorithm, describe the use of cqiv, and provide empirical examples. Keywords: cqiv, quantile regression, censored data, endogeneity, instrumental variable, control function File-URL: http://www.stata-journal.com/article.html?article=st0576 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893615 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0576/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:768-781 Template-Type: ReDIF-Article 1.0 Author-Name: Alexandra Blenkinsop Author-Workplace-Name: MRC Clinical Trials Unit at UCL Author-Email: Alexandra.Blenkinsop.16@ucl.ac.uk Author-Name: Babak Choodari-Oskooei Author-Workplace-Name: MRC Clinical Trials Unit at UCL Author-Email: b.choodari-oskooei@ucl.ac.uk Title: Multiarm, multistage randomized controlled trials with stopping boundaries for efficacy and lack of benefit: An update to nstage Journal: Stata Journal Pages: 782-802 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: Royston et al.’s (2011, Trials 12: 81) multiarm, multistage (MAMS) framework for the design of randomized clinical trials uses intermediate outcomes to drop research arms early for lack of benefit at interim stages, increasing effi- ciency in multiarm designs. However, additionally permitting interim evaluation of efficacy on the primary outcome measure could increase adoption of the de- sign and result in practical benefits, such as savings in patient numbers and cost, should any efficacious arm be identified early. The nstage command, which aids the design of MAMS trial designs, has been updated to support this methodolog- ical extension. Operating characteristics can now be calculated for a design with binding or nonbinding stopping rules for lack of benefit and with efficacy stopping boundaries. An additional option searches for a design that strongly controls the familywise error rate at the desired level. We illustrate how the new features can be used to design a trial with the drop-down menu, using the original comparisons from the MAMS trial STAMPEDE as an example. The new functionality of the command will serve a broader range of trial objectives and increase efficiency of the design and thus increase uptake of the MAMS design in practice. Keywords: nstage, nstagemenu, multiarm multistage, familywise error rate, efficacy stopping boundaries, adaptive designs File-URL: http://www.stata-journal.com/article.html?article=st0175_2 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893616 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0175_2/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:782-802 Template-Type: ReDIF-Article 1.0 Author-Name: Jennifer Thompson Author-Workplace-Name: London School of Hygiene and Tropical Medicine Author-Email: jennifer.thompson@lshtm.ac.uk Author-Name: Calum Davey Author-Workplace-Name: London School of Hygiene and Tropical Medicine Author-Email: calum.davey@lshtm.ac.uk Author-Name: Richard Hayes Author-Workplace-Name: London School of Hygiene and Tropical Medicine Author-Email: richard.hayes@lshtm.ac.uk Author-Name: James Hargreaves Author-Workplace-Name: London School of Hygiene and Tropical Medicine Author-Email: james.hargreaves@lshtm.ac.uk Author-Name: Katherine Fielding Author-Workplace-Name: London School of Hygiene and Tropical Medicine Author-Email: katherine.fielding@lshtm.ac.uk Title: Permutation tests for stepped-wedge cluster-randomized trials Journal: Stata Journal Pages: 803-819 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: Permutation tests are useful in stepped-wedge trials to provide robust statistical tests of intervention-effect estimates. However, the permute command does not produce valid tests in this setting because individual observations are not exchangeable. We introduce the swpermute command, which permutes clusters to sequences to maintain exchangeability. The command provides additional functionality for performing analyses of stepped-wedge trials. In particular, we include the withinperiod option, which performs the specified analysis separately in each period of the study with the resulting period-specific intervention-effect estimates combined as a weighted average. We also include functionality to test nonzero null hypotheses to aid in the construction of confidence intervals. Examples of the application of swpermute are given using data from a trial testing the impact of a new tuberculosis diagnostic test on bacterial confirmation of a tuberculosis diagnosis. Keywords: swpermute, stepped wedge, cluster randomized, permutation test, randomization test File-URL: http://www.stata-journal.com/article.html?article=st0577 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893624 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0577/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:803-819 Template-Type: ReDIF-Article 1.0 Author-Name: Ariel Linden Author-Workplace-Name: Linden Consulting Group Author-Email: alinden@lindenconsulting.org Author-Person: pli1113 Title: Assessing medication adherence using Stata Journal: Stata Journal Pages: 820-831 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: In this article, I introduce the medadhere command, which computes medication adherence rates for two commonly used measures in research and practice: the medication possession ratio and the proportion of days covered. medadhere computes adherence rates for a single medication or multiple medica- tions, and its options provide great flexibility to support the specific needs of the user. Keywords: medadhere, medication adherence, medication compliance, medication possession ratio, proportion of days covered, pharmacy claims File-URL: http://www.stata-journal.com/article.html?article=st0578 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893625 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0578/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:820-831 Template-Type: ReDIF-Article 1.0 Author-Name: David M. Kaplan Author-Workplace-Name: University of Missouri Author-Email: kaplandm@missouri.edu Author-Person: pka649 Title: distcomp: Comparing distributions Journal: Stata Journal Pages: 832-848 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: In this article, I introduce the distcomp command, which assesses whether two distributions differ at each possible value while controlling the probability of any false positive, even in finite samples. I discuss syntax and the underlying methodology (from Goldman and Kaplan [2018, Journal of Econometrics 206: 143–166]). Multiple examples illustrate the distcomp command, including revisiting the experimental data of Gneezy and List (2006, Econometrica 74: 1365–1384) and the regression discontinuity design of Cattaneo, Frandsen, and Titiunik (2015, Journal of Causal Inference 3: 1–24). Keywords: distcomp, familywise error rate, ksmirnov, regression discontinuity, treatment effects, comparing distributions File-URL: http://www.stata-journal.com/article.html?article=st0579 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893626 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0579/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:832-848 Template-Type: ReDIF-Article 1.0 Author-Name: Stanislav Anatolyev Author-Workplace-Name: CERGE-EI Author-Email: stanislav.anatolyev@cerge-ei.cz Author-Person: pan48 Author-Name: Alena Skolkova Author-Workplace-Name: CERGE-EI Author-Email: alena.skolkova@cerge-ei.cz Title: Many instruments: Implementation in Stata Journal: Stata Journal Pages: 849-866 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: In recent decades, econometric tools for handling instrumental-variable regressions characterized by many instruments have been developed. We introduce a command, mivreg, that implements consistent estimation and testing in linear instrumental-variables regressions with many (possibly weak) instruments. mivreg covers both homoskedastic and heteroskedastic environments, estimators that are both nonrobust and robust to error nonnormality and projection matrix limit, and parameter tests and specification tests both with and without correction for existence of moments. We also run a small simulation experiment using mivreg and illustrate how mivreg works with real data. Keywords: mivreg, instrumental-variables regression, many instruments, limited information maximum likelihood, Fuller correction, robust standard errors, specification testing File-URL: http://www.stata-journal.com/article.html?article=st0580 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893627 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0580/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:849-866 Template-Type: ReDIF-Article 1.0 Author-Name: Daniel Klein Author-Workplace-Name: International Centre for Higher Education Research Kassel Author-Email: klein@incher.uni-kassel.de Title: Extensions to the label commands Journal: Stata Journal Pages: 867-882 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: Data management tasks include manipulating variables, variable labels, and value labels. While Stata has versatile commands and functions to address the first task, managing variable and value labels is not as convenient. In this article, I introduce a new command, elabel, that enhances the capabilities of Stata’s label commands. I discuss these enhancements using various examples. I also demonstrate how to add new commands to elabel. Keywords: elabel, label, value labels, variable labels, data management File-URL: http://www.stata-journal.com/article.html?article=dm0101 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893630 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/dm0101/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:867-882 Template-Type: ReDIF-Article 1.0 Author-Name: Barbara Rossi Author-Workplace-Name: University Pompeu Fabra Author-Email: barbara.rossi@upf.edu Author-Person: pro86 Author-Name: Yiru Wang Author-Workplace-Name: University Pompeu Fabra Author-Email: syiru.wang@upf.edu Title: Vector autoregressive-based Granger causality test in the presence of instabilities Journal: Stata Journal Pages: 883-899 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: In this article, we review Granger causality tests that are robust to the presence of instabilities in a vector autoregressive framework. We also introduce the gcrobustvar command, which illustrates the procedure in Stata. In the presence of instabilities, the Granger causality robust test is more powerful than the traditional Granger causality test. Keywords: gcrobustvar, Granger causality, vector autoregressive, VAR, instability, structural breaks, local projections File-URL: http://www.stata-journal.com/article.html?article=st0581 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893631 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0581/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:883-899 Template-Type: ReDIF-Article 1.0 Author-Name: Ariel Gu Author-Workplace-Name: Northumbria University Author-Email: ariel.gu@northumbria.ac.uk Author-Name: Hong Il Yoo Author-Workplace-Name: Durham University Author-Email: h.i.yoo@durham.ac.uk Author-Person: pyo103 Title: vcemway: A one-stop solution for robust inference with multiway clustering Journal: Stata Journal Pages: 900-912 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: Most Stata commands allow cluster(varname) or vce(cluster clustvar) as an option, popularizing the use of standard errors that are robust to one-way clustering. For adjusting standard errors for multiway clustering, there is no solution that is as widely applicable. While several community-contributed packages support multiway clustering, each package is compatible only with a subset of models that Stata’s ever-expanding library of commands allows the researcher to fit. We introduce a command, vcemway, that provides a one-stop solution for multiway clustering. vcemway works with any estimation command that allows cluster(varname) as an option, and it adjusts standard errors, individual significance statistics, and confidence intervals in output tables for multiway clustering in specified dimensions. The covariance matrix used in making this adjustment is stored in e(V), meaning that any subsequent call to postestimation commands that use e(V) as input (for example, test and margins) will also produce results that are robust to multiway clustering. Keywords: vcemway, ivreg2, cmgreg, reghdfe, boottest, two-way clustering, multiway clustering File-URL: http://www.stata-journal.com/article.html?article=st0582 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893637 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0582/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:900-912 Template-Type: ReDIF-Article 1.0 Author-Name: E. Keith Smith Author-Workplace-Name: GESIS-Leibniz Institute for the Social Sciences Author-Email: keith.smith@gesis.org Author-Name: Michael G. Lacy Author-Workplace-Name: Colorado State University Author-Email: michael.lacy@colostate.edu Author-Name: Adam Mayer Author-Workplace-Name: Colorado State University Author-Email: adam.mayer@colostate.edu Title: Performance simulations for categorical mediation: Analyzing khb estimates of mediation in ordinal regression models Journal: Stata Journal Pages: 913-930 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: Standard mediation techniques for fitting mediation models cannot readily be translated to nonlinear regression models because of scaling issues. Methods to assess mediation in regression models with categorical and limited response variables have expanded in recent years, and these techniques vary in their approach and versatility. The recently developed khb technique purports to solve the scaling problem and produce valid estimates across a range of nonlinear regression models. Prior studies demonstrate that khb performs well in binary logistic regression models, but performance in other models has yet to be inves- tigated. In this article, we evaluate khb’s performance in fitting ordinal logistic regression models as an exemplar of the wider set of models to which it applies. We examined performance across 38,400 experimental conditions involving sample size, number of response categories, distribution of variables, and amount of medi- ation. Results indicate that under all experimental conditions, khb estimates the difference (mediation) coefficient and its associated standard error with little bias and that the nominal confidence interval coverage closely matches the actual. Our results suggest that researchers using khb can assume that the routine reasonably approximates population parameters. Keywords: khb, performance simulation, mediation, ordinal logistic regression File-URL: http://www.stata-journal.com/article.html?article=st0583 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893638 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0583/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:913-930 Template-Type: ReDIF-Article 1.0 Author-Name: Michael J. Crowther Author-Workplace-Name: University of Leicester Author-Email: michael.crowther@le.ac.uk Title: Multilevel mixed-effects parametric survival analysis: Estimation, simulation, and application Journal: Stata Journal Pages: 931-949 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: In this article, I present the community-contributed stmixed command for fitting multilevel survival models. It serves as both an alternative to Stata’s official mestreg command and a complimentary command with substantial extensions. stmixed can fit multilevel survival models with any number of levels and random effects at each level, including flexible spline-based approaches (such as Royston–Parmar and the log-hazard equivalent) and user-defined hazard models. Simple or complex time-dependent effects can be included, as can expected mortality for a relative survival model. Left-truncation (delayed entry) is supported, and t-distributed random effects are provided as an alternative to Gaussian random effects. I illustrate the methods with a commonly used dataset of patients with kidney disease suffering recurrent infections and a simulated ex- ample illustrating a simple approach to simulating clustered survival data using survsim (Crowther and Lambert 2012, Stata Journal 12: 674–687; 2013, Statis- tics in Medicine 32: 4118–4134). stmixed is part of the merlin family (Crowther 2017, arXiv Working Paper No. arXiv:1710.02223; 2018, arXiv Working Paper No. arXiv:1806.01615). Keywords: stmixed, stmixed postestimation, mestreg, multilevel survival models File-URL: http://www.stata-journal.com/article.html?article=st0584 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893639 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0584/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:931-949 Template-Type: ReDIF-Article 1.0 Author-Name: Oleg Badunenko Author-Workplace-Name: University of Portsmouth Author-Email: oleg.badunenko@port.ac.uk Author-Person: pba432 Author-Name: Harald Tauchmann Author-Workplace-Name: Friedrich-Alexander-Universität Erlangen-Nürnberg Author-Email: harald.tauchmann@fau.de Author-Person: pta144 Title: Simar and Wilson two-stage efficiency analysis for Stata Journal: Stata Journal Pages: 950-988 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: When one analyzes the determinants of production efficiency, regressing efficiency scores estimated by data envelopment analysis on explanatory variables has much intuitive appeal. Simar and Wilson (2007, Journal of Econometrics 136: 31–64) show that this conventional two-stage estimation procedure suffers from severe flaws that render its results, and particularly statistical inference based on them, questionable. They additionally propose a statistically grounded bootstrap- based two-stage estimator that eliminates the above-mentioned weaknesses of its conventional predecessors and comes in two variants. In this article, we introduce the new command simarwilson, which implements either variant of the suggested estimator in Stata. The command allows for various options and extends the orig- inal procedure in some respects. For instance, it allows for analyzing both output- and input-oriented efficiency. To demonstrate the capabilities of simarwilson, we use data from the Penn World Tables and the Global Competitiveness Report by the World Economic Forum to perform a cross-country empirical study about the importance of quality of governance in a country for its efficiency of output production. Keywords: simarwilson, simarwilson postestimation, gciget, Global Com- petitiveness Index, DEA, two-stage estimation, truncated regression, bootstrap, efficiency, bias correction, environmental variables File-URL: http://www.stata-journal.com/article.html?article=st0585 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893640 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/st0585/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:950-988 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: Some simple devices to ease the spaghetti problem Journal: Stata Journal Pages: 989-1008 Issue: 4 Volume: 19 Year: 2019 Month: December Abstract: The spaghetti problem arises in graphics when multiple time series or other functional traces show mostly a tangled mess. Devices to improve on graph- ical defaults include transformed scales (especially logarithmic scales); trying to increase the graph area showing the data (especially by losing the legend whenever possible); different colors sometimes; subdividing data into a few groups; subtrac- tion to focus on residuals or smoothing to reduce noise; selection or sampling of what is shown or emphasized; and stacking series vertically. Keywords: graphics, line plots, quantile plots, marker labels, panel data, longitudinal data File-URL: http://www.stata-journal.com/article.html?article=gr0080 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893641 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj19-4/gr0080/ Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:989-1008 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 133: Box plots that show median and quartiles only Journal: Stata Journal Pages: 1009-1014 Issue: 4 Volume: 19 Year: 2019 Month: December File-URL: http://www.stata-journal.com/article.html?article=gr0081 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893643 Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:1009-1014 Template-Type: ReDIF-Article 1.0 Author-Name: William H. Dow Author-Workplace-Name: University of California, Berkeley Author-Email: wdow@berkeley.edu Author-Person: pdo236 Author-Name: Edward C. Norton Author-Workplace-Name: University of Michigan Author-Email: ecnorton@umich.edu Author-Person: pno89 Author-Name: J. Travis Donahoe Author-Workplace-Name: Harvard University Author-Email: jtdonahoe@g.harvard.edu Title: Stata tip 134: Multiplicative and marginal interaction effects in nonlinear models Journal: Stata Journal Pages: 1015-1020 Issue: 4 Volume: 19 Year: 2019 Month: December File-URL: http://www.stata-journal.com/article.html?article=st0586 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893644 Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:1015-1020 Template-Type: ReDIF-Article 1.0 Author-Name: David A. Wagstaff Author-Workplace-Name: Pennsylvania State University Author-Email: daw22@psu.edu Author-Name: Ofer Harel Author-Workplace-Name: University of Connecticut Title: A closer examination of three small-sample approximations to the multiple-imputation degrees of freedom, erratum Journal: Stata Journal Pages: 1021 Issue: 4 Volume: 19 Year: 2019 Month: December File-URL: http://www.stata-journal.com/article.html?article=st0235_1 File-Function: link to article purchase X-DOI: 10.1177/1536867X19893645 Handle:RePEc:tsj:stataj:v:19:y:2019:i:4:p:1021