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 2022: Christopher F. Baum
Journal: Stata Journal
Pages: 727-733
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI: 10.1177/1536867X221140932
File-URL: http://www.stata-journal.com/article.html?article=gn0092
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Template-Type: ReDIF-Article 1.0
Author-Name: Takuya Hasebe
Author-Workplace-Name: Sophia University
Author-Email: thasebe@sophia.ac.jp 
Author-Person: pha1037
Title: Endogenous models of binary choice outcomes: Copula-based maximum-likelihood estimation and treatment effects
Journal: Stata Journal
Pages: 734-771
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI: 10.1177/1536867X221140943
Abstract: In this article, I describe the commands that implement the estimation of three endogenous models of binary choice outcome. The command esbinary fits the endogenously switching model, where a potential outcome differs across two treatment states. The command edbinary fits the endogenous dummy model, which includes a dummy variable indicating the treatment state as one of the explanatory variables. After one estimates the parameters of these models, various treatment effects can be estimated as postestimation statistics. The command ssbinary fits the sample-selection model, where an outcome is observed in only one of the states. The commands fit these models using copula-based maximum- likelihood estimation.
Keywords: esbinary, edbinary, ssbinary, endogeneity, treatment effects, binary outcome, copula-based maximum-likelihood estimation, endogenous switching model, sample selection
File-URL: http://www.stata-journal.com/article.html?article=st0691
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Template-Type: ReDIF-Article 1.0
Author-Name: Giovanni Cerulli
Author-Workplace-Name: IRCrES-CNR
Author-Email: giovanni.cerulli@ircres.cnr.it
Author-Person: pce40
Title: Machine learning using Stata/Python
Journal: Stata Journal
Pages: 772-810
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI: 10.1177/1536867X221140944
Abstract: I present two related commands, r_ml_stata_cv and c_ml_stata_cv, for fitting popular machine learning methods in both a regression and a classification setting. Using the recent Stata/Python integration platform introduced in Stata 16, these commands provide hyperparameters’ optimal tuning via K-fold cross-validation using grid search. More specifically, they use the Python Scikit- learn application programming interface to carry out both cross-validation and outcome/label prediction.
Keywords: r_ml_stata_cv, c_ml_stata_cv, get_test_train, machine learning, Python, optimal tuning
File-URL: http://www.stata-journal.com/article.html?article=pr0076
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Template-Type: ReDIF-Article 1.0
Author-Name:  John A. Gallis
Author-Workplace-Name: Duke University
Author-Email: john.gallis@duke.edu
Author-Name:  Xueqi Wang
Author-Workplace-Name: Yale School of Public Health
Author-Email: xueqi.wang@yale.edu
Author-Name:  Paul J. Rathouz
Author-Workplace-Name: University of Texas at Austin
Author-Email: paul.rathouz@austin.utexas.edu
Author-Name:  John S. Preisser
Author-Workplace-Name: University of North Carolina at Chapel Hill
Author-Email: jpreisse@bios.unc.edu
Author-Name:  Fan Li
Author-Workplace-Name: Yale School of Public Health
Author-Email: fan.f.li@yale.edu
Author-Name:  Elizabeth L. Turner
Author-Workplace-Name: Duke University
Author-Email: liz.turner@duke.edu
Title: power swgee: GEE-based power calculations in stepped wedge cluster randomized trials
Journal: Stata Journal
Pages: 811-841
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI: 10.1177/1536867X221140953
Abstract: Stepped wedge cluster randomized trials (SW-CRTs) are increasingly being used to evaluate interventions in medical, public health, educational, and social science contexts. With the longitudinal and crossover natures of an SW- CRT, complex analysis techniques are often needed, which makes appropriately powering SW-CRTs challenging. In this article, we introduce a newly developed SW-CRT power calculator embedded within the power command in Stata. The power calculator assumes a marginal model (that is, generalized estimating equa- tions) for the primary analysis of SW-CRTs, for which other currently available
 SW-CRT power calculators may not be suitable. The command accommodates complete cross-sectional and closed-cohort designs and includes multilevel correla- tion structures appropriate for such designs. We discuss the methods and formulas underlying our SW-CRT calculator and provide illustrative examples of the use of power swgee. We provide suggestions about the choice of parameters in power swgee and conclude by discussing areas of future research that may improve the command.
Keywords: power swgee, stepped wedge cluster randomized trials, staggered rollout designs, statistical power, group randomized trials, marginal models, generalized estimating equations
File-URL: http://www.stata-journal.com/article.html?article=st0692
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Template-Type: ReDIF-Article 1.0
Author-Name: Guanpeng Yan
Author-WorkPlace-Name: Shandong University
Author-Email: guanpengyan@yeah.net
Author-Name: Qiang Chen
Author-WorkPlace-Name: Shandong University
Author-Email: qiang2chen2@126.com
Author-Person: pch913
Title: rcm: A command for the regression control method
Journal: Stata Journal
Pages: 842-883
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI:  10.1177/1536867X221140960
Abstract: The regression control method, also known as the panel-data approach for program evaluation (Hsiao, Ching, and Wan, 2012, Journal of Applied Econo- metrics 27: 705–740; Hsiao and Zhou, 2019, Journal of Applied Econometrics 34: 463–481), is a convenient method for causal inference in panel data that ex- ploits cross-sectional correlation to construct counterfactual outcomes for a single treated unit by linear regression. In this article, we present the rcm command, which efficiently implements the regression control method with or without co- variates. Available methods for model selection include best subset, lasso, and forward stepwise and backward stepwise regression, while available selection cri- teria include the corrected Akaike information criterion, the Akaike information criterion, the Bayesian information criterion, the modified Bayesian information criterion, and cross-validation. Estimation and counterfactual predictions can be made by ordinary least squares, lasso, or postlasso ordinary least squares. For statistical inference, both the in-space placebo test using fake treatment units and the in-time placebo test using a fake treatment time can be implemented. The rcm command produces a series of graphs for visualization along the way. We demon- strate the use of the rcm command by revisiting classic examples of political and economic integration between Hong Kong and mainland China (Hsiao, Ching, and Wan 2012) and German reunification (Abadie, Diamond, and Hainmueller, 2015, American Journal of Political Science 59: 495–510).
Keywords: rcm, regression control method, panel-data approach, program evaluation, causal inference, counterfactual outcomes
File-URL: http://www.stata-journal.com/article.html?article=st0693
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Template-Type: ReDIF-Article 1.0
Author-Name: Noori Akhtar-Danesh
Author-Workplace-Name: McMaster University
Author-Email: daneshn@mcmaster.ca
Author-Name: Stephen C. Wingreen
Author-Workplace-Name: University of Canterbury
Author-Email: wingreen@bellsouth.net
Title: qpair: A command for analyzing paired Q-sorts in Q-methodology
Journal: Stata Journal
Pages: 884-907
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI: 10.1177/1536867X221141002
Abstract: In this article, we introduce qpair as a new command written in Stata for the analysis of paired Q-sorts in Q-methodology, which is used for studying subjective issues and is a combination of qualitative and quantitative techniques. The quantitative component of Q-methodology employs a by-person factor anal- ysis technique. However, currently there is no systematic approach for analyzing paired Q-sorts or longitudinal data in Q-methodology. We introduce the only statistical command available for the analysis of paired Q-sorts. The qpair com- mand employs the factor extraction and factor rotation techniques in Stata. The command is illustrated using a dataset representing perceptions of 50 information technology professionals on person–organization fit regarding their training and development priorities.
Keywords: qpair, Q-methodology, by-person factor analysis, paired Q-sort, person–organization fit
File-URL: http://www.stata-journal.com/article.html?article=st0694
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Template-Type: ReDIF-Article 1.0
Author-Name: Xiangmei Ma
Author-Email: xiangmei.ma@duke-nus.edu.sg
Author-WorkPlace-Name: Duke–NUS Medical School
Author-Name: Yin Bun Cheung
Author-Email: yinbun.cheung@duke-nus.edu.sg
Author-WorkPlace-Name: Duke–NUS Medical School
Title: crtrest: A command for ratio estimators of intervention effects on event rates in cluster randomized trials
Journal: Stata Journal
Pages: 908-923
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI: 10.1177/1536867X221141012
Abstract: We describe five asymptotically unbiased estimators of intervention effects on event rates in nonmatched and matched-pair cluster randomized trials, and we present a bias-corrected version of the estimators for use when the number of clusters is small. The estimators are the ratio of mean counts (r1), ratio of mean cluster-level event rates (r2), ratio of event rates (r3), double ratio of counts (r4), and double ratio of event rates (r5). r1, r2, and r3 estimate the total effect, which comprises the direct and indirect effects; r4 and r5 estimate the direct effect. We describe a new command, crtrest, that provides these ratio estimators and their standard errors in nonmatched and matched-pair cluster randomized trials.
Keywords: crtrest, ratio estimator, intervention effects, event rate, cluster randomized trial
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Template-Type: ReDIF-Article 1.0
Author-Name: Jia Li
Author-Email: jiali@smu.edu.sg
Author-WorkPlace-Name: Singapore Management University
Author-Name: Zhipeng Liao
Author-Email: zhipeng.liao@ucla.edu
Author-WorkPlace-Name: University of California–Los Angeles
Author-Name: Rogier Quaedvlieg
Author-Email: rogier.quaedvlieg@ecb.europa.eu
Author-WorkPlace-Name: European Central Bank
Author-Person: pqu119
Author-Name: Wenyu Zhou
Author-Email: wenyuzhou@intl.zju.edu.cn
Author-WorkPlace-Name: Zhejiang University
Title: Conditional evaluation of predictive models: The cspa command
Journal: Stata Journal
Pages: 924-940
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI: 10.1177/1536867X221141014
Abstract: In this article, we introduce a new command, cspa, that implements the conditional superior predictive ability test developed in Li, Liao, and Quaed- vlieg (2022, Review of Economic Studies 89: 843–875). With the conditional per- formance of predictive methods measured nonparametrically by the conditional expectation functions of their predictive losses, we test the null hypothesis that a benchmark model weakly outperforms a collection of competitors uniformly across the conditioning space. The proposed command can implement this test for both independent cross-sectional data and serially dependent time-series data. Confi- dence sets for the most superior model can be obtained by inverting the test, for which the cspa command also offers a convenient implementation.
Keywords: cspa, conditional moment inequality, forecast evaluation, functional inference, series estimation
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Template-Type: ReDIF-Article 1.0
Author-Name: Hongbing Zhu
Author-Email: zhuhongbing@hhu.edu.cn
Author-WorkPlace-Name: Hohai University
Author-Name: Lihua Yang
Author-Email: yanglihua@hhu.edu.cn
Author-WorkPlace-Name: Hohai University
Title: portfolio: A command for conducting portfolio analysis in Stata
Journal: Stata Journal
Pages: 941-957
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI: 10.1177/1536867X221141021
Abstract: Portfolio analysis is widely used in empirical asset pricing to explore the cross-sectional relation between two or more variables. In this article, we introduce the methodology of portfolio analysis and describe a new command, portfolio, that provides a one-step solution for portfolio analysis. portfolio calculates the equal- or value-weighted returns with a t statistic for the portfolio and tests the significance of a long-short strategy in portfolios. portfolio also provides the Newey–West standard-error adjustment option for alleviating the impact of potential autocorrelation and heteroskedasticity in financial time series.
Keywords: portfolio, portfolio analysis, nonparametric analysis, hedging strategy
File-URL: http://www.stata-journal.com/article.html?article=st0697
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Template-Type: ReDIF-Article 1.0
Author-Name: Max D. Weinreb
Author-Email: maxweinreb@utexas.edu
Author-WorkPlace-Name: University of Texas at Austin
Author-Name: Jenny Trinitapoli
Author-Email: jennytrini@uchicago.edu
Author-WorkPlace-Name: University of Chicago
Title: printcase: A command for visualizing single observations
Journal: Stata Journal
Pages: 958-968
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI:  10.1177/1536867X221141022
Abstract: In this article, we introduce the printcase command, which outputs data from a specific observation into an easy-to-read Microsoft Word or PDF docu- ment. printcase allows analysts to focus on a single observation within a dataset and view that observation in its entirety. The output displays fields in table format, with all variables identified by their corresponding labels and all responses iden- tified by their corresponding value labels. We explain how printcase works, give examples of circumstances under which this type of table-based quasiquestionnaire would be useful, and provide code for printing single observations.
Keywords: printcase, survey research, fieldwork, data quality, interviewer training
File-URL: http://www.stata-journal.com/article.html?article=dm0109
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Template-Type: ReDIF-Article 1.0
Author-Name: Matteo Bottai
Author-Email: matteo.bottai@ki.se
Author-WorkPlace-Name: Karolinska Institutet
Title: Estimating the risk of events with stprisk
Journal: Stata Journal
Pages: 969-974
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI:   10.1177/1536867X221141057
Abstract: Incidence rates are popular summary measures of the occurrence over time of events of interest. They are also called mortality rates or failure rates, depending on the context. The incidence rate is defined as the ratio between the total number of events and total follow-up time and can be estimated with the strate command. When the event of interest can occur multiple times on any given subject over a time period, like infections, the incidence rate represents an average count per unit of time, such as the average number of infections per year. When the event of interest can occur only once, such as death, an alternative summary measure is the risk, or probability, of occurrence per unit time, such as the risk of dying in one year. In this article, I present the stprisk command, which estimates risks, and illustrate its use and interpretation through a data example.
Keywords:  stprisk, incidence rates, mortality rates, survival analysis
File-URL: http://www.stata-journal.com/article.html?article=st0698
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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: Automating axis labels: Nice numbers and transformed scales
Journal: Stata Journal
Pages: 975-995
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI: 10.1177/1536867X221141058
Abstract: Two common problems with graph axis labels are to decide in advance on some “nice” numbers to use on one or both axes and to show particular labels on some transformed scale. In this column, I discuss the nicelabels and mylabels commands, which address these problems. The first command is new to Stata, and the second is a revision of a previously published command. I also survey the myticks command for tick placement. In all commands, the main output is a local macro in the calling program’s space, in the interest of promoting automation in do-files and programs.
Keywords: nicelabels, mylabels, myticks, axis labels, axis ticks, axis scales, transformations, graphics
File-URL: http://www.stata-journal.com/article.html?article=gr0092
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Template-Type: ReDIF-Article 1.0
Author-Name: Roger Newson 
Author-Workplace-Name: King’s College London
Author-Email: roger.newson@kcl.ac.uk
Author-Person: pne37
Title: Stata tip 147: Porting downloaded packages between machines
Journal: Stata Journal
Pages: 996-997
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI: 10.1177/1536867X221141067
File-URL: http://www.stata-journal.com/article.html?article=st0699
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Handle:RePEc:tsj:stataj:v:22:y:2022:i:4:p:996-997

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 148: Searching for words within strings
Journal: Stata Journal
Pages: 998-1003
Issue: 4
Volume: 22
Year: 2022
Month: December
DOI: 10.1177/1536867X221141068
File-URL: http://www.stata-journal.com/article.html?article=dm0110
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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: 1004
Issue: 4
Volume: 22
Year: 2022
Month: December 
DOI:  10.1177/1536867X211063150
Abstract: Updates for previously published packages are provided.
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