Template-Type: ReDIF-Article 1.0
Author-Name: Paul A. Tiffin
Author-Workplace-Name: University of York
Author-Email: paul.tiffin@york.ac.uk 
Title: miesize: Effect-size calculation in imputed data
Journal: Stata Journal
Pages: 503-513
Issue: 3
Volume: 24
Year: 2024
Month: September
Abstract: In this article, I describe the miesize command for the calculation of effect sizes in imputed data. There may be situations where an effect size needs to be estimated for an intervention, an exposure, or a group membership variable but data on the independent or dependent variable are missing. Such missing data are commonly dealt with by multiply imputing plausible values. However, in this circumstance, the estimated effect size and associated standard errors will need to be pooled and estimated from the imputed dataset. The miesize command auto- mates this process and calculates effect sizes for a binary variable from multiply imputed data in wide format. The estimates and standard errors (used to calculate the confidence intervals) are recombined using Rubin’s (1987, Multiple Imputation for Nonresponse in Surveys [Wiley]) rules. These rules are applied such that the average point estimate for the effect size is calculated from the imputed datasets. The pooled standard error, and hence confidence intervals, is calculated to account for both the variance between the imputed datasets and the variance within them. Pooled effect sizes and confidence intervals for Cohen’s (1988, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. [Lawrence Erlbaum]) 𝑑, Hedges’s (1981, Journal of Educational Statistics 6: 107–128) 𝑔, and Glass’s (Smith and Glass, 1977, American Psychologist 32: 752–760) delta are provided by miesize.
Keywords: miesize, effect size, imputation, Rubin’s rules, Cohen’s 𝑑, Hedges’s 𝑔, Glass’s delta
File-URL: http://www.stata-journal.com/article.html?article=st0755
File-Function: link to article purchase
X-DOI: 10.1177/1536867X241276113
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Handle:RePEc:tsj:stataj:v:24:y:2024:i:3:p:503-513

Template-Type: ReDIF-Article 1.0
Author-Name: Filippo Palomba
Author-Workplace-Name: Princeton University
Author-Email: fpalomba@princeton.edu
Title: Getting away from the cutoff in regression discontinuity designs
Journal: Stata Journal
Pages: 371-401
Issue: 3
Volume: 24
Year: 2024
Month: September
Abstract: Regression discontinuity (RD) designs are highly popular in economic research because of their strong internal validity and straightforward intuition. While RD estimates are local in nature, several recent articles propose methods that generalize RD estimates to units outside a small neighborhood of the cutoff. In this article, I introduce the getaway package, which implements the method proposed by Angrist and Rokkanen (2015, Journal of the American Statistical As- sociation 110: 1331–1344) to extrapolate treatment-effect estimates “away from the cutoff”, relying on a classical unconfoundedness condition. Additionally, the package features a data-driven algorithm designed to identify a set of covariates that fulfills the unconfoundedness assumption. It also incorporates a toolkit in- tended for testing and visualization purposes.
Keywords: getaway, ciasearch, ciatest, ciares, ciacs, getawayplot, regression discontinuity designs, treatment effects
File-URL: http://www.stata-journal.com/article.html?article=st0751
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X-DOI: 10.1177/1536867X241276108
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Handle:RePEc:tsj:stataj:v:24:y:2024:i:3:p:371-401

Template-Type: ReDIF-Article 1.0
Author-Name: Chuntao Li
Author-Workplace-Name: Henan University
Author-Email: chtl@henu.edu.cn
Author-Name: Yizhuo Fang
Author-Workplace-Name: Henan University
Author-Email: yzhfang1@163.com
Author-Name: Lifang Cao
Author-Workplace-Name: Henan University
Author-Email: caolifang64@163.com
Title: cnevent: Event study with Chinese equity market data
Journal: Stata Journal
Pages: 478-502
Issue: 3
Volume: 24
Year: 2024
Month: September
Abstract:  In this article, we present a new command, cnevent, that runs event studies about Chinese-listed companies. With cnevent, researchers are required to provide only a list of events with the Chinese stock code and the corresponding date for each event, and the command can automatically extract indexes and each individual stock’s return data to run the whole process of the event study. Furthermore, cnevent enables users to choose the benchmark from among different market indexes and different event window sets with options. The command then generates daily abnormal returns for all trading days within the event window and aggregates the cumulative abnormal returns (CARs) for the whole event window. Finally, cnevent can plot a graph to show the trend of the CAR𝑡 within the event window and test whether the event has a significant effect on valuation.
Keywords: cnevent, Chinese listed companies, event study, abnormal returns, cumulative abnormal returns, CARs
File-URL: http://www.stata-journal.com/article.html?article=st0754
File-Function: link to article purchase
X-DOI: 10.1177/1536867X241276112
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Handle:RePEc:tsj:stataj:v:24:y:2024:i:3:p:478-502

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: Quantile–quantile plots, generalized
Journal: Stata Journal
Pages: 513-534
Issue: 3
Volume: 24
Year: 2024
Month: September
Abstract: Quantile–quantile plots in the precise sense of scatterplots showing corresponding quantiles of two variables have long been supported by official command qqplot. That command is generalized here in several ways in a new command, qqplotg. In this article, I explain the major features of qqplotg and give several examples of its use. Themes include the use of quantile–quantile plots to explore the possibilities for working on a transformed scale and the value of plotting difference between quantiles versus mean quantile or plotting position. Various historical and methodological remarks are sprinkled throughout.
Keywords: qqplotg, quantile–quantile plots, quantile plots, quantiles, plotting positions, additivity, homoskedasticity, symmetry, linearity, transformations, difference versus mean, graphics, distributions
File-URL: http://www.stata-journal.com/article.html?article=gr0096
File-Function: link to article purchase
DOI: 10.1177/1536867X231196519
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Handle:RePEc:tsj:stataj:v:24:y:2024:i:3:p:513-534

Template-Type: ReDIF-Article 1.0
Author-Name: Marcel F. Jonker
Author-Workplace-Name: Erasmus University Rotterdam
Author-Email: marcel@mfjonker.com
Title: Fitting garbage class mixed logit models in Stata
Journal: Stata Journal
Pages: 427-445
Issue: 3
Volume: 24
Year: 2024
Month: September
Abstract: In this article, I describe the garbage_mixl command, which fits the garbage class and standard panel mixed logit models in Stata.
Keywords: garbage_mixl, garbage class, mixlogit, mixed logit, random parameter logit
File-URL: http://www.stata-journal.com/article.html?article=st0753
File-Function: link to article purchase
X-DOI:  10.1177/1536867X241276110
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Handle:RePEc:tsj:stataj:v:24:y:2024:i:3:p:427-445

Template-Type: ReDIF-Article 1.0
Author-Name: Kerui Du
Author-Workplace-Name: Xiamen University
Author-Email: kerrydu@xmu.edu.cn
Author-Name: Luis Orea
Author-Workplace-Name: University of Oviedo
Author-Email: lorea@uniovi.es
Author-Person: por56
Author-Name: Inmaculada C. Álvarez
Author-Workplace-Name: Universidad Autónoma de Madrid
Author-Email: inmaculada.alvarez@uam.es
Author-Person: pal460
Title: Fitting spatial stochastic frontier models in Stata
Journal: Stata Journal
Pages: 402-426
Issue: 3
Volume: 24
Year: 2024
Month: September
Abstract: In this article, we introduce a new command, xtsfsp, for fitting spatial stochastic frontier models in Stata. Over the last decades, stochastic frontier models have seen important theoretical progress via the incorporation of various types of spatial components. Models that can account for spatial dependence and spillovers have been developed for efficiency and productivity analysis, drawing extensive attention from industry and academia. Because of the unavailability of the statistical packages, the empirical applications of the new stochastic frontier models appear to be lagging. The xtsfsp command provides a procedure for fitting spatial stochastic frontier models in the style of Orea and Álvarez (2019, Journal of Econometrics 213: 556–577) and Galli (2023, Spatial Economic Analysis 18: 239–258), enabling users to handle different sources of spatial dependence. In this article, we introduce spatial stochastic frontier models, describing the syntax and options of the new command and providing several examples to illustrate its usage.
Keywords: xtsfsp, stochastic frontier models, SFA, spatial dependence, technical efficiency, spillovers
File-URL: http://www.stata-journal.com/article.html?article=st0752
File-Function: link to article purchase
X-DOI: 10.1177/1536867X241276109
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Handle:RePEc:tsj:stataj:v:24:y:2024:i:3:p:402-426

Template-Type: ReDIF-Article 1.0
Author-Name: David M. Drukker
Author-Workplace-Name: Clemson University
Author-Email: ddrukke@clemson.edu
Author-Person: pdr184
Author-Name: Kevin S. S. Henning
Author-Workplace-Name: Sam Houston State University
Author-Email: henning@shsu.edu
Author-Name: Christian Raschke
Author-Workplace-Name: Sam Houston State University
Author-Email: raschke@shsu.edu
Author-Person: pra598
Title: Tests and confidence bands for multiple one-sided comparisons
Journal: Stata Journal
Pages: 446-477
Issue: 3
Volume: 24
Year: 2024
Month: September
Abstract: One-sided inference should be used in some applications, but Stata has limited support for one-sided tests and confidence intervals for a single compari- son and almost no support for one-sided tests and confidence bands for multiple comparisons. In this article, we provide an introduction to one-sided tests and confidence intervals for a single hypothesis and to one-sided tests and confidence intervals for multiple comparisons. We also discuss extensions of the sotable com- mand introduced in Drukker (2023, Stata Journal 23: 518–544) to cover one-sided tests and confidence bands for a single comparison and for multiple comparisons. We also provide examples of how to use sotable to perform multiple tests against values other than zero and how to perform multiple tests after commands like margins and nlcom that support the post option.
Keywords:  sotable, one-sided testing, multiple comparisons, confidence bands, simultaneous inference, multiple testing, max-𝑡, simultaneous tests
File-URL: http://www.stata-journal.com/article.html?article=st0718_1
File-Function: link to article purchase
X-DOI: 10.1177/1536867X241276111
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Handle:RePEc:tsj:stataj:v:24:y:2024:i:3:p:446-477

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 156: Concentration and diversity measures using egen
Journal: Stata Journal
Pages: 535-545
Issue: 2
Volume: 23
Year: 2024
Month: September
File-URL: http://www.stata-journal.com/article.html?article=st0756
File-Function: link to article purchase
DOI: 10.1177/1536867X241276115
Handle:RePEc:tsj:stataj:v:24:y:2024:i:3:p:535-545

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 157: Adding extra lines to graphs
Journal: Stata Journal
Pages: 546-550
Issue: 2
Volume: 23
Year: 2024
Month: September
File-URL: http://www.stata-journal.com/article.html?article=gr0097
File-Function: link to article purchase
DOI: 10.1177/1536867X241276115
Handle:RePEc:tsj:stataj:v:24:y:2024:i:3:p:546-550