Template-Type: ReDIF-Article 1.0 Author-Name: Ben Jann Author-Workplace-Name: University of Bern Author-Email: ben.jann@soz.unibe.ch Title: Customizing Stata graphs made easy (part 1) Journal: Stata Journal Pages: 491-502 Issue: 3 Volume: 18 Year: 2018 Month: September Abstract: The overall look of Stata’s graphs is determined by so-called scheme files. Scheme files are system components; that is, they are part of the local Stata installation. In this article, I argue that style settings deviating from default schemes should be part of the script producing the graphs, rather than being kept in separate scheme files, and I present a simple tool called grstyle that supports such practice. Keywords: grstyle, graph, graphics, scheme files File-URL: http://www.stata-journal.com/article.html?article=gr0073 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/gr0073/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:3:p:491-502 Template-Type: ReDIF-Article 1.0 Author-Name: Mark D. Chatfield Author-Workplace-Name: University of Queensland Author-Email: m.chatfield@uq.edu.au Title: Graphing each individual’s data over time Journal: Stata Journal Pages: 503-516 Issue: 3 Volume: 18 Year: 2018 Month: September Abstract: Graphing each individual’s data over time (in separate graphs) can be a worthwhile approach in exploring longitudinal and panel datasets. This especially applies for datasets where several variables change over time and where there are many possible time points, for example, administrative datasets and patient safety profiles in clinical trials. Studying a few individuals’ graphs closely can provide insight into the nature and quality of the data, generate hypotheses, and inform data analysis. Selecting a few typical or unusual graphs can make for powerful presentations at meetings. I give examples of graphing a single variable and multiple variables over time for each individual, and I detail associated Stata coding tips and tricks. Keywords: longitudinal data, panel data, time series, graphics, twoway, scatter, putdocx, superimposition, xtline, patient profile, profile plot, developmental trajectory File-URL: http://www.stata-journal.com/article.html?article=gr0074 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/gr0074/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:3:p:503-516 Template-Type: ReDIF-Article 1.0 Author-Name: Vincenzo Verardi Author-Email: vverardi@unamur.be Author-WorkPlace-Name: Université de Namur Author-Name: Catherine Vermandele Author-Email: vermande@ulb.ac.be Author-WorkPlace-Name: Université libre de Bruxelles Title: Univariate and multivariate outlier identification for skewed or heavy-tailed distributions Journal: Stata Journal Pages: 517-532 Issue: 3 Volume: 18 Year: 2018 Month: September Abstract: In univariate and in multivariate analyses, it is difficult to identify outliers in the case of skewed or heavy-tailed distributions. In this article, we propose simple univariate and multivariate outlier identification procedures that perform well with these types of distributions while keeping the computational complexity low. We describe the commands gboxplot (univariate case) and sdasym (multivariate case), which implement these procedures in Stata. Keywords: gboxplot, sdasym, box plot, generalized box plot, outlier detection, outlyingness, projection, Tukey g-and-h distribution File-URL: http://www.stata-journal.com/article.html?article=st0533 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/st0533/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:3:p:517-532 Template-Type: ReDIF-Article 1.0 Author-Name: Shawna K. Metzger Author-Workplace-Name: College of William & Mary Author-Email: skmetzger@wm.edu Author-Name: Benjamin T. Jones Author-Workplace-Name: University of Mississippi Author-Email: btjones1@olemiss.edu Title: mstatecox: A package for simulating transition probabilities from semiparametric multistate survival models Journal: Stata Journal Pages: 533-563 Issue: 3 Volume: 18 Year: 2018 Month: September Abstract: Multistate duration models are a valuable tool used in multiple fields to examine how subjects move through a series of discrete phases and stages. The models themselves may be fit using common statistical software, but their broader adoption has been limited because of a lack of software to substantively interpret their results. Transition probabilities are the common postestimation quantity for interpreting multistate duration model results. De Wreede, Fiocco, and Putter’s (2011, Journal of Statistical Software 38(7): 1–30) mstate package provides R with the functionality to estimate these quantities from semiparametric multistate models, yet no Stata equivalent exists for semiparametric models. We introduce a new set of Stata commands to meet this need. Our mstatecox suite calculates transition probabilities from semiparametric multistate duration models with simulation. It can accommodate any configuration of stages and also has the ability to accommodate time-interacted covariates. We demonstrate our package’s functionality using de Wreede, Fiocco, and Putter’s European Registry of Blood and Marrow Transplantation example dataset. Keywords: mstatecox, mstcovar, mstphtest, msttvc, mstdraw, mstsample, mstutil, multistate, transition probabilities, multistate duration, semiparametric multistate survival models File-URL: http://www.stata-journal.com/article.html?article=st0534 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/st0534/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:3:p:533-563 Template-Type: ReDIF-Article 1.0 Author-Name: Jordy Meekes Author-Workplace-Name: Utrecht University School of Economics Author-Email: jordy.meekes@unimelb.edu.au Author-Name: Wolter H. J. Hassink Author-Workplace-Name: Utrecht University School of Economics Author-Email: W.H.J.Hassink@uu.nl Title: flowbca: A flow-based cluster algorithm in Stata Journal: Stata Journal Pages: 564-584 Issue: 3 Volume: 18 Year: 2018 Month: September Abstract: In this article, we introduce the Stata implementation of a flow-based cluster algorithm, flowbca, written in Mata. The main purpose of flowbca is to identify clusters based on relational data of flows. We illustrate the command by providing multiple examples of applications from the research fields of economic geography, industrial input–output analysis, and social network analysis. Keywords: flowbca, clusters, aggregation, flows, regions, industries, economic geography, input–output analysis, social network analysis File-URL: http://www.stata-journal.com/article.html?article=st0535 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/st0535/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:3:p:564-584 Template-Type: ReDIF-Article 1.0 Author-Name: Jan Ditzen Author-Workplace-Name: Heriot-Watt University Author-Email: j.ditzen@hw.ac.uk Title: Estimating dynamic common-correlated effects in Stata Journal: Stata Journal Pages: 585-617 Issue: 3 Volume: 18 Year: 2018 Month: September Abstract: In this article, I introduce a new command, xtdcce2, that fits a dynamic common-correlated effects model with heterogeneous coefficients in a panel with a large number of observations over cross-sectional units and time periods. The estimation procedure mainly follows Chudik and Pesaran (2015b, Journal of Econometrics 188: 393–420) but additionally supports the common correlated effects estimator (Pesaran, 2006, Econometrica 74: 967–1012), the mean group estimator (Pesaran and Smith, 1995, Journal of Econometrics 68: 79–113), and the pooled mean group estimator (Pesaran, Shin, and Smith, 1999, Journal of the American Statistical Association, 94: 621–634). xtdcce2 allows heterogeneous or homogeneous coefficients and supports instrumental-variable regressions and un- balanced panels. The cross-sectional dependence test is automatically calculated and presented in the estimation output. Small-sample time-series bias can be cor- rected by “half-panel” jackknife correction or recursive mean adjustment. I carry out a simulation to prove the estimator’s consistency. Keywords: xtdcce2, xtcd2, parameter heterogeneity, dynamic panels, cross-section dependence, common correlated effects, pooled mean group estimator, mean group estimator, instrumental variables, ivreg2 File-URL: http://www.stata-journal.com/article.html?article=st0536 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/st0536/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:3:p:585-617 Template-Type: ReDIF-Article 1.0 Author-Name: Gabriele Rovigatti Author-Workplace-Name: University of Chicago Author-Email: gabriele.rovigatti@gmail.com Author-Name: Vincenzo Mollisi Author-Workplace-Name: Free University of Bozen–Bolzano Author-Email: vincenzo.mollisi@gmail.com Title: Theory and practice of total-factor productivity estimation: The control function approach using Stata Journal: Stata Journal Pages: 618-662 Issue: 3 Volume: 18 Year: 2018 Month: September Abstract: Alongside instrumental-variables and fixed-effects approaches, the control function approach is the most widely used in production function estima- tion. Olley and Pakes (1996, Econometrica 64: 1263–1297), Levinsohn and Petrin (2003, Review of Economic Studies 70: 317–341), and Ackerberg, Caves, and Frazer (2015, Econometrica 83: 2411–2451) have all contributed to the field by proposing two-step estimation procedures, whereas Wooldridge (2009, Economics Letters 104: 112–114) showed how to perform a consistent estimation within a single-step generalized method of moments framework. In this article, we pro- pose a new estimator based on Wooldridge’s estimation procedure, using dynamic panel instruments `a la Blundell and Bond (1998, Journal of Econometrics 87: 115– 143), and we evaluate its performance by using Monte Carlo simulations. We also present the new command prodest for production function estimation, and we show its main features and strengths in a comparative analysis with other community-contributed commands. Finally, we provide evidence of the numeri- cal challenges faced when using the Olley–Pakes and Levinsohn–Petrin estimators with the Ackerberg–Caves–Frazer correction in empirical applications, and we doc- ument how the generalized method of moments estimates vary depending on the optimizer or starting points used. Keywords: prodest, production functions, productivity, MrEst, dynamic panel GMM File-URL: http://www.stata-journal.com/article.html?article=st0537 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/st0537/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:3:p:618-662 Template-Type: ReDIF-Article 1.0 Author-Name: Damian Clarke Author-Workplace-Name: Universidad de Santiago de Chile Author-Email: damian.clarke@usach.cl Author-Name: Benjamín Matta Author-Workplace-Name: Universidad de Santiago de Chile Author-Email: benjamin.matta@usach.cl Title: Practical considerations for questionable IVs Journal: Stata Journal Pages: 663-691 Issue: 3 Volume: 18 Year: 2018 Month: September Abstract: In this article, we examine several techniques that allow for the con- struction of bounds estimates based on instrumental variables, even when the instruments are not valid. We introduce the plausexog and imperfectiv com- mands, which implement methods described by Conley, Hansen, and Rossi (2012, Review of Economics and Statistics 94: 260–272) and Nevo and Rosen (2012b, Review of Economics and Statistics 94: 659–671). We examine the performance of these bounds under a range of circumstances, which leads to several practical results related to the informativeness of the bounds in different situations. Keywords: plausexog, imperfectiv, instrumental variables, exclusion re- strictions, invalidity, plausibly exogenous, imperfect IVs File-URL: http://www.stata-journal.com/article.html?article=st0538 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/st0538/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:3:p:663-691 Template-Type: ReDIF-Article 1.0 Author-Name: Long Hong Author-Workplace-Name: University of Wisconsin Author-Email: long.hong@wisc.edu Author-Name: Guido Alfani Author-Workplace-Name: Bocconi University Author-Email: guido.alfani@unibocconi.it Author-Name: Chiara Gigliarano Author-Workplace-Name: University of Insubria Author-Email: chiara.gigliarano@uninsubria.it Author-Name: Marco Bonetti Author-Workplace-Name: Bocconi University Author-Email: marco.bonetti@unibocconi.it Title: giniinc: A Stata package for measuring inequality from incomplete income and survival data Journal: Stata Journal Pages: 692-715 Issue: 3 Volume: 18 Year: 2018 Month: September Abstract: Often, observed income and survival data are incomplete because of left- or right-censoring or left- or right-truncation. Measuring inequality (for instance, by the Gini index of concentration) from incomplete data like these will produce biased results. We describe the package giniinc, which contains three independent commands to estimate the Gini concentration index under different conditions. First, survgini computes a test statistic for comparing two (survival) distributions based on the nonparametric estimation of the restricted Gini index for right-censored data, using both asymptotic and permutation inference. Second, survbound computes nonparametric bounds for the unrestricted Gini index from censored data. Finally, survlsl implements maximum likelihood estimation for three commonly used parametric models to estimate the unrestricted Gini index, both from censored and truncated data. We briefly discuss the methods, describe the package, and illustrate its use through simulated data and examples from an oncology and a historical income study. Keywords: survgini, survbound, survlsl, Gini index, income distribution, inequality, survival analysis, censored data, truncated data File-URL: http://www.stata-journal.com/article.html?article=st0539 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/st0539/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:3:p:692-715 Template-Type: ReDIF-Article 1.0 Author-Name: Anna Chaimani Author-Workplace-Name: Paris Descartes University Author-Email: anna.chaimani@parisdescartes.fr Author-Name: Dimitris Mavridis Author-Email: dmavridi@cc.uoi.gr Author-Workplace-Name: University of Ioannina Author-Name: Julian P. T. Higgins Author-Email: julian.higgins@bristol.ac.uk Author-Workplace-Name: University of Bristol Author-Name: Georgia Salanti Author-Email: georgia.salanti@ispm.unibe.ch Author-Workplace-Name: University of Bern Author-Name: Ian R. White Author-Email: ian.white@ucl.ac.uk Author-Workplace-Name: MRC Biostatistics Unit Title: Allowing for informative missingness in aggregate data meta-analysis with continuous or binary outcomes: Extensions to metamiss Journal: Stata Journal Pages: 716-740 Issue: 3 Volume: 18 Year: 2018 Month: September Abstract: Missing outcome data can invalidate the results of randomized trials and their meta-analysis. However, addressing missing data is often a challenging issue because it requires untestable assumptions. The impact of missing outcome data on the meta-analysis summary effect can be explored by assuming a rela- tionship between the outcome in the observed and the missing participants via an informative missingness parameter. The informative missingness parameters can- not be estimated from the observed data, but they can be specified, with associated uncertainty, using evidence external to the meta-analysis, such as expert opinion. The use of informative missingness parameters in pairwise meta-analysis of ag- gregate data with binary outcomes has been previously implemented in Stata by the metamiss command. In this article, we present the new command metamiss2, which is an extension of metamiss for binary or continuous data in pairwise or network meta-analysis. The command can be used to explore the robustness of results to different assumptions about the missing data via sensitivity analysis. Keywords: metamiss2, informative missingness, mixed treatment comparison, sensitivity analysis, meta-analysis File-URL: http://www.stata-journal.com/article.html?article=st0540 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/st0540/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:3:p:716-740 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: Tables as lists: From rounding to binning Journal: Stata Journal Pages: 741-754 Issue: 3 Volume: 18 Year: 2018 Month: September Abstract: This is a basic review of how to bin variables in Stata, meaning how to divide their range or support into disjoint intervals. I survey rounding functions with emphasis on floor and ceiling functions as tools to get clearly defined intervals of equal width. Using a specific display format is usually a better idea than rounding to multiples of a fraction. Quantile binning is popular in several fields. I give tips and tricks on how to produce such bins and also on how to show their limitations. Experimentation with the display command or Mata is a good way to learn about functions and to test binning rules. Keywords: binning, rounding, format, display, quantiles File-URL: http://www.stata-journal.com/article.html?article=dm0095 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/dm0095/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:3:p:741-754 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 130: 106610 and all that: Date variables that need to be fixed Journal: Stata Journal Pages: 755-757 Issue: 3 Volume: 18 Year: 2018 Month: September File-URL: http://www.stata-journal.com/article.html?article=dm0096 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/dm0096/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:3:p:755-757 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 758-759 Issue: 3 Volume: 18 Year: 2018 Month: September Abstract: Updates for previously published packages are provided. Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/st0375_1/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/st0376_2/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/st0427_2/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/st0524_1/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj18-3/st0527_1/ Note: Windows users should not attempt to download these files with a web browser. Handle:RePEc:tsj:stataj:y:18:y:2018:i:3:p:758-759