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 2020: Daniel Klein Journal: Stata Journal Pages: 759-762 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976310 File-URL: http://www.stata-journal.com/article.html?article=gn0083 File-Function: link to article purchase Handle:RePEc:tsj:stataj:v:20:y:2020:i:4:p:759-762 Template-Type: ReDIF-Article 1.0 Author-Name: Michael J. Crowther Author-Workplace-Name: University of Leicester Author-Workplace-Name: Karolinska Intitutet Author-Email: michael.crowther@le.ac.uk Title: merlin—A unified modeling framework for data analysis and methods development in Stata Journal: Stata Journal Pages: 763-784 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976311 Abstract: The challenges in statistics and data science are rapidly growing be- cause access to a multitude of data types continues to increase, as well as the sheer quantity of data. Analysts are now presented with multivariate data, sometimes measured repeatedly, and often requiring the ability to model nonlinear relation- ships and hierarchical structures. In this article, I present the merlin command, which attempts to provide an extremely general framework for data analysis. From simple settings such as fitting a linear regression model or a Weibull survival model to more complex settings such as fitting a three-level logistic mixed-effects model or a multivariate joint model of multiple longitudinal outcomes (of different types) and a recurrent event and survival with nonlinear effects, merlin can fit them all. I will take a single dataset and attempt to show you the full range of capabilities of merlin and discuss some future directions for the implementation in Stata. Keywords: merlin, modeling framework, outcome models, survival models, longitudinal models, Gaussian, Bernoulli, beta, Poisson, ordinal logistic, ordinal probit, gamma, exponential, Gompertz, Royston–Parmar, log-hazard, Weibull, time-dependent effects, restricted cubic splines, B-splines, fractional polynomial, random effects, multilevel, multivariate, hierarchical File-URL: http://www.stata-journal.com/article.html?article=st0616 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/st0616/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:4:p:763-784 Template-Type: ReDIF-Article 1.0 Author-Name: Federico Belotti Author-Workplace-Name: University of Rome Tor Vergata Author-Email: federico.belotti@uniroma2.it Author-Person: pbe427 Author-Name: Franco Peracchi Author-Workplace-Name: University of Rome Tor Vergata Author-Email: franco.peracchi@uniroma2.it Author-Person: ppe19 Title: Fast leave-one-out methods for inference, model selection, and diagnostic checking Journal: Stata Journal Pages: 785-804 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976312 Abstract: In this article, we describe jackknife2, a new prefix command for jackknifing linear estimators. It takes full advantage of the available leave-one-out formula, thereby allowing for substantial reduction in computing time. Of special note is that jackknife2 allows the user to compute cross-validation and diagnos- tic measures that are currently not available after ivregress 2sls, xtreg, and xtivregress. Keywords: jackknife2, jackknife, heteroskedasticity-consistent standard errors, cross-validation, diagnostic checking, predictive residuals File-URL: http://www.stata-journal.com/article.html?article=st0617 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/st0617 Handle:RePEc:tsj:stataj:y:18:y:2018:i:4:p:785-804 Template-Type: ReDIF-Article 1.0 Author-Name: Yuan Xue Author-Workplace-Name: Huazhong University of Science and Technology Author-Email: lsxueyuan19920310@163.com Author-Name: Chuntao Li Author-Workplace-Name: Henan University Author-Email: chtl@henu.edu.cn Author-Person: pli762 Title: Extracting Chinese geographic data from Baidu Map API Journal: Stata Journal Pages: 805-811 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976313 Abstract: In this article, we describe the two new commands cngcode and cnaddress, which can be used to link Chinese addresses to locations defined by their longitudes and latitudes through Baidu Map API v3.0 (http://api.map. baidu.com), an online map and navigation system widely used in China. cngcode transfers Chinese addresses to locations, whereas cnaddress does the opposite. These two commands make it easier with Stata to deal with addresses and locations in China. Keywords: cngcode, cnaddress, China, location, Baidu Map File-URL: http://www.stata-journal.com/article.html?article=dm0104 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/dm0104/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:4:p:805-811 Template-Type: ReDIF-Article 1.0 Author-Name: Damian Clarke Author-WorkPlace-Name: Universidad de Chile Author-Email: dclarke@fen.uchile.cl Author-Person: pcl102 Author-Name: Joseph P. Romano Author-WorkPlace-Name: Stanford University Author-Email: romano@stanford.edu Author-Name: Michael Wolf Author-WorkPlace-Name: University of Zurich Author-Email: michael.wolf@econ.uzh.ch Author-Person: pwo206 Title: The Romano–Wolf multiple-hypothesis correction in Stata Journal: Stata Journal Pages: 812-843 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976314 Abstract: When considering multiple-hypothesis tests simultaneously, standard statistical techniques will lead to overrejection of null hypotheses unless the multi- plicity of the testing framework is explicitly considered. In this article, we discuss the Romano–Wolf multiple-hypothesis correction and document its implementa- tion in Stata. The Romano–Wolf correction (asymptotically) controls the fami- lywise error rate, that is, the probability of rejecting at least one true null hy- pothesis among a family of hypotheses under test. This correction is considerably more powerful than earlier multiple-testing procedures, such as the Bonferroni and Holm corrections, given that it takes into account the dependence structure of the test statistics by resampling from the original data. We describe a command, rwolf, that implements this correction and provide several examples based on a wide range of models. We document and discuss the performance gains from us- ing rwolf over other multiple-testing procedures that control the familywise error rate. Keywords: rwolf, bootstrap, familywise error rate, multiple-hypothesis testing, permutation methods, rwolf, stepdown procedure File-URL: http://www.stata-journal.com/article.html?article=st0618 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/st0618/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:4:p:812-843 Template-Type: ReDIF-Article 1.0 Author-Name: Giovanni Cerulli Author-Email: giovanni.cerulli@ircres.cnr.it Author-WorkPlace-Name: Research Institute on Sustainable Economic Growth, National Research Council of Italy Author-Person: pce40 Title: Nonparametric synthetic control using the npsynth command Journal: Stata Journal Pages: 844-865 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976315 Abstract: In this article, I build on the work of Abadie and Gardeazabal (2003, American Economic Review 93: 113–132) and Abadie, Diamond, and Hainmueller (2010, Journal of the American Statistical Association 105: 493–505), extend- ing the synthetic control method for program evaluation—implemented in Stata via the community-contributed command synth—to the case of a nonparametric identification of the synthetic (or counterfactual) time pattern of a treated unit (a country, a region, a city, etc.) subject to a specific intervention in a given time. After theoretical description of the model, I present npsynth, the command I de- veloped for estimating the nonparametric synthetic control method proposed in this article. Using both simulated and real data, I set out a comparison of the performance of the parametric and nonparametric methods and widely discuss the results. Keywords: npsynth, synthetic control, nonparametric estimation, program evaluation File-URL: http://www.stata-journal.com/article.html?article=st0619 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/st0619/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:4:p:844-865 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: Rocío Titiunik Author-Email: titiunik@princeton.edu Author-WorkPlace-Name: Princeton University Author-Person: pti260 Author-Name: Gonzalo Vazquez-Bare Author-Email: gvazquez@econ.ucsb.edu Author-WorkPlace-Name: University of California, Santa Barbara Title: Analysis of regression-discontinuity designs with multiple cutoffs or multiple scores Journal: Stata Journal Pages: 866-891 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976320 Abstract: In this article, we introduce the Stata (and R) package rdmulti, which consists of three commands (rdmc, rdmcplot, rdms) for analyzing regression- discontinuity (RD) designs with multiple cutoffs or multiple scores. The command rdmc applies to noncumulative and cumulative multicutoff RD settings. It calcu- lates pooled and cutoff-specific RD treatment effects and provides robust bias- corrected inference procedures. Postestimation and inference is allowed. The command rdmcplot offers RD plots for multicutoff settings. Finally, the com- mand rdms concerns multiscore settings, covering in particular cumulative cutoffs and two running variable contexts. It also calculates pooled and cutoff-specific RD treatment effects, provides robust bias-corrected inference procedures, and allows for postestimation and inference. These commands use the Stata (and R) package rdrobust for plotting, estimation, and inference. Companion R functions with the same syntax and capabilities are provided. Keywords: rdmulti, rdmc, rdmcplot, rdms, regression discontinuity designs, multiple cutoffs, multiple scores, local polynomial methods File-URL: http://www.stata-journal.com/article.html?article=st0620 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/st0620/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:4:p:866-891 Template-Type: ReDIF-Article 1.0 Author-Name: Kristoffer Bjärkefur Author-Workplace-Name: World Bank, Development Economics Research Author-Email: kbjarkefur@worldbank.org Author-Name: Luíza Cardoso de Andrade Author-Workplace-Name: World Bank, Development Economics Research Author-Email: lcardoso@worldbank.org Author-Name: Benjamin Daniels Author-Workplace-Name: Georgetown University Author-Email: benjamin.daniels@georgetown.edu Author-Person: pda505 Title: iefieldkit: Commands for primary data collection and cleaning Journal: Stata Journal Pages: 892-915 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976321 Abstract: Data collection and cleaning workflows implement highly repetitive but extremely important processes. In this article, we introduce iefieldkit, a package developed to standardize and simplify best practices for high-quality primary data collection across the World Bank’s Development Research Group Impact Evalua- tions department. iefieldkit automates error-checking for electronic Open Data Kit-based survey modules such as those implemented in SurveyCTO; duplicate checking and resolution; data cleaning, including renaming, labeling, recoding, and survey harmonization; and codebook creation. Keywords: iefieldkit, iecodebook, iecompdup, ieduplicates, ietestform, primary data collection, ODK, SurveyCTO, data cleaning, survey harmonization, duplicates, codebooks File-URL: http://www.stata-journal.com/article.html?article=dm0105 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/dm0105/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:4:p:892-915 Template-Type: ReDIF-Article 1.0 Author-Name: Andrew Q. Philips Author-Workplace-Name: University of Colorado Boulder Author-Email: andrew.philips@colorado.edu Title: An easy way to create duration variables in binary cross-sectional time-series data Journal: Stata Journal Pages: 916-930 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976322 Abstract: In cross-sectional time-series data with a dichotomous dependent variable, failing to account for duration dependence when it exists can lead to faulty inferences. A common solution is to include duration dummies, polynomials, or splines to proxy for duration dependence. Because creating these is not easy for the common practitioner, I introduce a new command, mkduration, that is a straightforward way to generate a duration variable for binary cross-sectional time-series data in Stata. mkduration can handle various forms of missing data and allows the duration variable to easily be turned into common parametric and nonparametric approximations. Keywords: mkduration, binary cross-sectional time series, event history, duration File-URL: http://www.stata-journal.com/article.html?article=st0621 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/st0621/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:4:p:916-930 Template-Type: ReDIF-Article 1.0 Author-Name: E. F. Haghish Author-Workplace-Name: Department of Psychology, University of Oslo Author-Email: info@haghish.com Title: Developing, maintaining, and hosting Stata statistical software on GitHub Journal: Stata Journal Pages: 931-951 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976323 Abstract: The popularity of GitHub is growing, among not only software developers but also statisticians and data scientists. In this article, I discuss why social coding platforms such as GitHub are preferable for developing, documenting, maintaining, and collaborating on statistical software. Furthermore, I introduce the github command version 2.0 for Stata, which facilitates building, searching, installing, and managing statistical packages hosted on GitHub. I also provide a command for searching filenames in all Stata packages published on the Statistical Software Components Archive and GitHub to ensure unique filenames and pack- age names, which is a common concern among Stata programmers. I make further suggestions to enhance the practice of developing and hosting statistical packages on GitHub as well as using them for data analysis. Keywords: github, version control, social coding, social computing, statis- tics software, data mining File-URL: http://www.stata-journal.com/article.html?article=pr0073 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/pr0073/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:4:p:931-951 Template-Type: ReDIF-Article 1.0 Author-Name: Benjamin Schwab Author-Workplace-Name: Kansas State University Author-Email: benschwab@ksu.edu Author-Person: psc356 Author-Name: Sarah Janzen Author-Workplace-Name: University of Illinois at Urbana Champaign Author-Email: sjanzen@illinois.edu Author-Person: pja593 Author-Name: Nicholas P. Magnan Author-Workplace-Name: University of Georgia Author-Email: nmagnan@uga.edu Author-Person: pma1625 Author-Name: William M. Thompson Author-Workplace-Name: IDInsight Author-Email: will.thompson@idinsight.org Title: Constructing a summary index using the standardized inverse-covariance weighted average of indicators Journal: Stata Journal Pages: 952-964 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976325 Abstract: Researchers often want to examine the relationship between a variable of interest and multiple related outcomes. To avoid problems of inference that arise from testing multiple hypotheses, one can create a summary index of the outcomes. Summary indices facilitate generalizing findings and can be more powerful than individual tests. In this article, we introduce a command, swindex, that implements the generalized least-squares method of index construction pro- posed by Anderson (2008, Journal of the American Statistical Association 103: 1481–1495). We describe the command and its options and provide an example based on Blattman, Fiala, and Martinez’s (2014, Quarterly Journal of Economics 129: 697–752) evaluation of a cash transfer program in Uganda. Keywords: swindex, index construction, GLS File-URL: http://www.stata-journal.com/article.html?article=st0622 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/st0622/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:4:p:952-964 Template-Type: ReDIF-Article 1.0 Author-Name: Laura Magazzini Author-Workplace-Name: Sant’Anna School of Advanced Studies Author-Email: laura.magazzini@santannapisa.it Author-Person: pma1141 Author-Name: Randolph Luca Bruno Author-Workplace-Name: University College London Author-Email: randolph.bruno@ucl.ac.uk Author-Person: pbr134 Author-Name: Marco Stampini Author-Workplace-Name: Inter-American Development Bank Author-Email: mstampini@iadb.org Author-Person: pst273 Title: Using information from singletons in fixed-effects estimation: xtfesing Journal: Stata Journal Pages: 965-975 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976326 Abstract: In this article, we describe the xtfesing command. The command implements a generalized method of moments estimator that allows exploiting singleton information in fixed-effects panel-data regression as in Bruno, Magazzini, and Stampini (2020, Economics Letters 186: Article 108519). Keywords: xtfesing, panel data, fixed effects, singletons, estimation efficiency File-URL: http://www.stata-journal.com/article.html?article=st0623 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/st0623/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:4:p:965-975 Template-Type: ReDIF-Article 1.0 Author-Name: Kerui Du Author-Workplace-Name: Xiamen University Author-Email: kerrydu@xmu.edu.cn Author-Name: Yonghui Zhang Author-Workplace-Name: Renmin University of China Author-Email: yonghui.zhang@hotmail.com Author-Name: Qiankun Zhou Author-Workplace-Name: Louisiana State University Author-Email: qzhou@lsu.edu Author-Person: pzh732 Title: Fitting partially linear functional-coefficient panel-data models with Stata Journal: Stata Journal Pages: 976-998 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976339 Abstract: In this article, we describe the implementation of fitting partially linear functional-coefficient panel models with fixed effects proposed by An, Hsiao, and Li [2016, Semiparametric estimation of partially linear varying coefficient panel data models in Essays in Honor of Aman Ullah (Advances in Econometrics, Volume 36)] and Zhang and Zhou (Forthcoming, Econometric Reviews). Three new commands xtplfc, ivxtplfc, and xtdplfc are introduced and illustrated through Monte Carlo simulations to exemplify the effectiveness of these estimators. Keywords: xtplfc, ivxtplfc, xtdplfc, functional coefficients, panel data, fixed effects, endogenous variables, sieve, spline File-URL: http://www.stata-journal.com/article.html?article=st0624 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/st0624/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:4:p:976-998 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: Loops, again and again Journal: Stata Journal Pages: 999-1015 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976340 Abstract: Two commands in official Stata, foreach and forvalues, provide structures for looping through lists of values (variable names, numbers, arbitrary text) and repeating commands using members of those lists in turn. These com- mands may be used interactively, and none is restricted to use in Stata programs. They are explained and compared in some detail with a variety of examples. In addition, a self-contained exposition is given on local macros, understanding of which is needed for use of foreach and forvalues. This column is a revision of the column “How to face lists with fortitude”, which appeared in Stata Journal 2: 202–222 (2002). (The bizarre bibliographical details are too, too extraordinary not to be flagged but were pure happenstance.) The presentation here has been trimmed of now historic content and corrected, improved, and updated in several minor details. Keywords: foreach, forvalues, lists, local macros, substitution first File-URL: http://www.stata-journal.com/article.html?article=pr0074 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/pr0074/ Handle:RePEc:tsj:stataj:y:18:y:2018:i:4:p:999-1015 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 139: The by() option of graph can work better than graph combine Journal: Stata Journal Pages: 1016-1027 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976341 File-URL: http://www.stata-journal.com/article.html?article=gr0085 File-Function: link to article purchase Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/gr0085/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:4:p:1016-1027 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 1028-1030 Issue: 4 Volume: 20 Year: 2020 Month: December DOI: 10.1177/1536867X20976342 Abstract: Updates for previously published packages are provided. Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/dm0042_3/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/dm0085_2/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/gr0072_1/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/pr0041_3/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/st0519_1/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/st0516_2/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/st0564_1/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-4/st0585_1/ Note: Windows users should not attempt to download these files with a web browser. Handle:RePEc:tsj:stataj:v:20:y:2020:i:4:p:1028-1030