Template-Type: ReDIF-Article 1.0 Author-Name: David Dale Author-Email: dale.david@yandex.ru Author-Name: Andrei Sirchenko Author-Workplace-Name: University of Amsterdam Author-Email: andrei.sirchenko@gmail.com Author-Person: psi424 Title: Estimation of nested and zero-inflated ordered probit models Journal: Stata Journal Pages: 3-38 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000002 Abstract: We introduce three new commands—nop, ziop2, and ziop3—for the estimation of a three-part nested ordered probit model, the two-part zero-inflated ordered probit models of Harris and Zhao (2007, Journal of Econometrics 141: 1073–1099) and Brooks, Harris, and Spencer (2012, Economics Letters 117: 683–686), and a three-part zero-inflated ordered probit model of Sirchenko (2020, Studies in Nonlinear Dynamics and Econometrics 24: 1) for ordinal outcomes, with both exogenous and endogenous switching. The three-part models allow the probabilities of positive, neutral (zero), and negative outcomes to be generated by distinct processes. The zero-inflated models address a preponderance of zeros and allow them to emerge in different latent regimes. We provide postestimation commands to compute probabilistic predictions and various measures of their accuracy, to assess the goodness of fit, and to perform model comparison using the Vuong test (Vuong, 1989, Econometrica 57: 307–333) with the corrections based on the Akaike and Schwarz information criteria. We investigate the finite-sample performance of the maximum likelihood estimators by Monte Carlo simulations, discuss the relations among the models, and illustrate the new commands with an empirical application to the U.S. federal funds rate target. Keywords: nop, ziop2, ziop3, ordinal outcomes, zero inflation, nested ordered probit, zero-inflated ordered probit, endogenous switching, Vuong test, federal funds rate target File-URL: http://hdl.handle.net/10.1177/1536867X211000002 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0625/ Handle:RePEc:tsj:stataj:v:21:y:2021:i:1:p:3-38 Template-Type: ReDIF-Article 1.0 Author-Name: Miguel Dorta Author-Workplace-Name: StataCorp Author-Email: jlgallup@pdx.edu Author-Name: Gustavo Sanchez Author-Workplace-Name: StataCorp Author-Email: gsanchez@stata.com Title: Bootstrap unit-root test for random walk with drift: The bsrwalkdrift command Journal: Stata Journal Pages: 39-50 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000003 Abstract: In this article, we introduce the command bsrwalkdrift, which is primarily intended to perform a bootstrap unit-root test under the null hypothesis of random walk with drift. The method implemented in this command is considerably more precise than the corresponding case of the conventional augmented Dickey–Fuller test, which can be inaccurate when the true value of the drift term is small relative to the standard deviation of the innovations. The command also has an option to account for deterministic linear trend and another option to perform bootstrap unit-root tests under the null hypothesis of random walk without drift. Keywords: xtavplot, xtavplots, added-variable plot, panel data, postestimation diagnostics, xtreg File-URL: http://hdl.handle.net/10.1177/1536867X211000003 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0626/ Handle:RePEc:tsj:stataj:v:21:y:2021:i:1:p:39-50 Template-Type: ReDIF-Article 1.0 Author-Name: Tore Bersvendsen Author-Workplace-Name: University of Agder, Kristiansand, Norway Author-Email: tore.Bersvendsen@kristiansand.kommune.no Author-Name: Jan Ditzen Author-Workplace-Name: Free University of Bozen-Bolzano Author-Email: jan.ditzen@unibz.it Author-Person: pdi434 Title: Testing for slope heterogeneity in Stata Journal: Stata Journal Pages: 51-80 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000004 Abstract: In this article, we introduce a new community-contributed command, xthst, to test for slope heterogeneity in panels with many observations over cross-sectional units and time periods. The command implements such a test, the delta test (Pesaran and Yamagata, 2008, Journal of Econometrics 142: 50–93). Under its null, slope coefficients are homogeneous across cross-sectional units. Under the alternative, slope coefficients are heterogeneous in the cross-sectional dimension. xthst also includes two extensions. The first is a heteroskedasticity- and autocorrelation-consistent robust test along the lines of Blomquist and Westerlund (2013, Economics Letters 121: 374–378). The second extension is a cross-sectional-dependence robust version. We discuss all tests and present examples using an economic growth model. A Monte Carlo simulation shows that the size and the power behave as expected. Keywords: xthst, parameter heterogeneity, fixed effects, pooled OLS, mean-group estimator, cross-section dependence, heterogeneity, common correlated random effects File-URL: http://hdl.handle.net/10.1177/1536867X211000004 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0627/ Handle:RePEc:tsj:stataj:v:21:y:2021:i:1:p:51-80 Template-Type: ReDIF-Article 1.0 Author-Name: Fernando Rios-Avila Author-Workplace-Name: Levy Economics Institute of Bard College Author-Email: friosavi@levy.org Author-Person: pri214 Title: Estimation of marginal effects for models with alternative variable transformations Journal: Stata Journal Pages: 81-96 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000005 Abstract: margins is a powerful postestimation command that allows the estimation of marginal effects for official and community-contributed commands, with well-defined predicted outcomes (see predict). While the use of factor-variable notation allows one to easily estimate marginal effects when interactions and polynomials are used, estimation of marginal effects when other types of transformations such as splines, logs, or fractional polynomials are used remains a challenge. In this article, I describe how margins’s capabilities can be extended to analyze other variable transformations using the command f_able. Keywords: f_able, margins, marginal effects, predict, variable transformations, nonlinear File-URL: http://hdl.handle.net/10.1177/1536867X211000005 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0628/ Handle:RePEc:tsj:stataj:v:21:y:2021:i:1:p:81-96 Template-Type: ReDIF-Article 1.0 Author-Name: Valentyn Litvin Author-Workplace-Name: Northwestern University Author-Email: jvalentynlitvin@u.northwestern.edu Author-Name: Charles F. Manski Author-Workplace-Name: Northwestern University Author-Email: cfmanski@u.northwestern.edu Author-Person: pma111 Title: Evaluating the maximum regret of statistical treatment rules with sample data on treatment response Journal: Stata Journal Pages: 97-122 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000006 Abstract: In this article, we present the wald_tc command, which computes the maximum regret (MR) of a user-specified statistical treatment rule that uses sample data on realized treatment response (and optionally an instrumental variable) to determine a treatment choice for a population. Because the outcomes of counterfactual treatments are not observed and treatment selection in the study population may not be random, decision makers may be able only to partially identify average treatment effects. wald_tc allows users to compute the MR of a proposed statistical treatment rule under a flexible specification of the data-generating process and determines the state that generates MR. Keywords: wald_tc, maximum regret, average treatment effect, instrumen- tal variable, partial identification File-URL: http://hdl.handle.net/10.1177/1536867X211000006 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0629/ Handle:RePEc:tsj:stataj:v:21:y:2021:i:1:p:97-122 Template-Type: ReDIF-Article 1.0 Author-Name: Masayuki Hirukawa Author-Workplace-Name: Ryukoku University Author-Email: hirukawa@econ.ryukoku.ac.jp Author-Name: Di Lu Author-Workplace-Name: StataCorp Author-Email: dliu@stata.com Author-Name: Artem Prokhorov Author-Workplace-Name: University of Sydney Business School Author-Email: artem.prokhorov@sydney.edu.au Author-Person: ppr133 Title: msreg: A command for consistent estimation of linear regression models using matched data Journal: Stata Journal Pages: 123-140 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000008 Abstract: Economists often use matched samples, especially when dealing with earning data where some observations are missing in one sample and need to be imputed from another sample. Hirukawa and Prokhorov (2018, Journal of Econometrics 203: 344–358) show that the ordinary least-squares estimator using matched samples is inconsistent and propose two consistent estimators. We de- scribe a new command, msreg, that implements these two consistent estimators based on two samples. The estimators attain the parametric convergence rate if the number of continuous matching variables is no greater than four. Keywords: msreg, bias correction, linear regression, matching estimation File-URL: http://hdl.handle.net/10.1177/1536867X211000008 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0630/ Handle:RePEc:tsj:stataj:v:21:y:2021:i:1:p:123-140 Template-Type: ReDIF-Article 1.0 Author-Name: Fausto Pacicco Author-Workplace-Name: LIUC–Università Carlo Cattaneo Author-Email: fpacicco@liuc.it Author-Person: ppa1199 Author-Name: Luigi Vena Author-Workplace-Name: LIUC–Università Carlo Cattaneo Author-Email: lvena@liuc.it Author-Name: Andrea Venegoni Author-Workplace-Name: LIUC–Università Carlo Cattaneo Author-Email: avenegoni@liuc.it Title: From common to firm-specific event dates: A new version of the estudy command Journal: Stata Journal Pages: 141-151 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000010 Abstract: The estudy command proposed by Pacicco, Vena, and Venegoni (2018, Stata Journal 18: 461–476) performs event studies only for event-date clustering, that is, when the event date is common to all securities. This constitutes a relevant limitation because the vast majority of this methodology’s applications concerns studies in which the events happen on different dates for each statistical unit considered. In this article, we propose and describe a substantial update to estudy, which 1) performs event studies in the absence of event-date clustering (that is, when each security has its own event date); 2) further customizes the output by producing LATEX-formatted tables; 3) graphs the cumulative abnormal returns over a customized period set by the user; 4) makes more output data available through either the return list or Excel files; 5) allows a double possibility as input: either prices or returns; and 6) uses wildcards. Keywords: estudy, event study, financial econometrics File-URL: http://hdl.handle.net/10.1177/1536867X211000010 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0532_2/ Handle:RePEc:tsj:stataj:v:21:y:2021:i:1:p:141-151 Template-Type: ReDIF-Article 1.0 Author-Name: Xavier D’Haultfoeuille Author-Workplace-Name: CREST Author-Email: xavier.dhaultfoeuille@ensae.fr Author-Person: pdh29 Author-Name: Lucas Girard Author-Workplace-Name: CREST Author-Email: lucas.girard@ensae.fr Author-Name: Roland Rathelot Author-Workplace-Name: University of Warwick Author-Email: r.rathelot@warwick.ac.uk Author-Person: pra238 Title: segregsmall: A command to estimate segregation in the presence of small units Journal: Stata Journal Pages: 152-179 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000018 Abstract: Suppose that a population, composed of a minority and a majority group, is allocated into units, which can be neighborhoods, firms, classrooms, etc. Qualitatively, there is some segregation whenever allocation leads to the concentration of minority individuals in some units more than in others. Quantitative measures of segregation have struggled with the small-unit bias. When units contain few individuals, indices based on the minority shares in units are upward biased. For instance, they would point to a positive amount of segregation even when allocation is strictly random. The command segregsmall implements three recent methods correcting for such bias: the nonparametric, partial identification approach of D’Haultfoeuille and Rathelot (2017, Quantitative Economics 8: 39–73); the parametric model of Rathelot (2012, Journal of Business & Economic Statistics 30: 546–553); and the linear correction of Carrington and Troske (1997, Journal of Business & Economic Statistics 15: 402–409). The package also allows for con-ditional analyses, namely, measures of segregation accounting for characteristics of the individuals or the units. Keywords: segregation indices, small-unit bias, partial identification, Dun- can index, Theil index, Atkinson index, Coworker index, Gini index File-URL: http://hdl.handle.net/10.1177/1536867X211000018 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0631/ Handle:RePEc:tsj:stataj:v:21:y:2021:i:1:p:152-179 Template-Type: ReDIF-Article 1.0 Author-Name: Juan D. Díaz Author-Workplace-Name: University of Chile Author-Email: juadiaz@fen.uchile.cl Author-Name: Iván Gutiérrez Author-Workplace-Name: Pontifical Catholic University of Chile Author-Email: isgutierrez@mat.uc.cl Author-Name: Jorge Rivera Author-Workplace-Name: University of Chile Author-Email: jrivera@econ.uchile.cl Author-Person: pri202 Title: Implementing blopmatching in Stata Journal: Stata Journal Pages: 180-194 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000021 Abstract: The blopmatching estimator for average treatment effects in observational studies is a nonparametric matching estimator proposed by Díaz, Rau, and Rivera (2015, Review of Economics and Statistics 97: 803–812). This approach uses the solutions of linear programming problems to build the weighting schemes that are used to impute the missing potential outcomes. In this article, we describe blopmatch, a new command that implements these estimators. Keywords: blopmatch, average treatment effects, matching, linear pro- gramming, synthetic covariate File-URL: http://hdl.handle.net/10.1177/1536867X211000021 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0632/ Handle:RePEc:tsj:stataj:v:21:y:2021:i:1:p:180-194 Template-Type: ReDIF-Article 1.0 Author-Name: W. Scott Comulada Author-Workplace-Name: University of California, Los Angeles Author-Email: wcomulada@mednet.ucla.edu Title: Calculating level-specific SEM fit indices for multilevel mediation analyses Journal: Stata Journal Pages: 195-205 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000022 Abstract: Stata’s gsem command provides the ability to fit multilevel structural equation models (SEM) and related multilevel models. A motivating example is provided by multilevel mediation analyses (MA) conducted on patient data from Methadone Maintenance Treatment clinics in China. Multilevel MA conducted through the gsem command examined the mediating effects of patients’ treatment progression and rapport with counselors on their treatment satisfaction. Multilevel models accounted for the clustering of patient observations within clinics. SEM fit indices, such as the comparative fit index and the root mean squared error of approximation, are commonly used in the SEM model selection process. Multilevel models present challenges in constructing fit indices because there are multiple levels of hierarchy to account for in establishing goodness of fit. Level-specific fit indices have been proposed in the literature but have not been incorporated into the gsem command. I created the gsemgof command to fill this role. Model results from the gsem command are used to calculate the level-specific comparative fit index and root mean squared error of approximation fit indices. I illustrate the gsemgof command through multilevel MA applied to two-level Methadone Maintenance Treatment data. Keywords: gsemgof, gsem, sem, multilevel, structural equation model, mediation analysis, fit index File-URL: http://hdl.handle.net/10.1177/1536867X211000022 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0633/ Handle:RePEc:tsj:stataj:y:19:y:2019:i:1:p:195-205 Template-Type: ReDIF-Article 1.0 Author-Name: Ercio Muñoz Author-Workplace-Name: Stone Center on Socio-Economic Inequality at CUNY Graduate Center Author-Email: emunozsaavedra@gc.cuny.edu Author-Name: Salvatore Morelli Author-Workplace-Name: University of Roma Tre Author-Email: smorelli@gc.cuny.edu Author-Person: pmo879 Title: kmr: A command to correct survey weights for unit nonresponse using groups’ response rates Journal: Stata Journal Pages: 206-219 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000025 Abstract: In this article, we describe kmr, a command to estimate a microcompliance function using groups’ nonresponse rates (Korinek, Mistiaen, and Ravallion, 2007, Journal of Econometrics 136: 213–235), which can be used to correct survey weights for unit nonresponse. We illustrate the use of kmr with an empirical example using the current population survey and state-level nonresponse rates. Keywords: gsemgof, gsem, sem, multilevel, structural equation model, mediation analysis, fit index File-URL: http://hdl.handle.net/110.1177/1536867X211000025 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0634/ Handle:RePEc:tsj:stataj:y:19:y:2019:i:1:p:206-219 Template-Type: ReDIF-Article 1.0 Author-Name: Jinjing Li Author-Workplace-Name: NATSEM, University of Canberra Author-Email: jinjing.li@canberra.edu.au Author-Name: Michael J. Zyphur Author-Workplace-Name: Business & Economics, University of Melbourne Author-Email: mzyphur@unimelb.edu.au Author-Name: George Sugihara Author-Workplace-Name: Scripps Institution of Oceanography, UCSD Author-Email: gsugihara@ucsd.edu Author-Name: Patrick J. Laub Author-Workplace-Name: Business & Economics, University of Melbourne Author-Email: patrick.laub@unimelb.edu.au Title: Beyond linearity, stability, and equilibrium: The edm package for empirical dynamic modeling and convergent cross-mapping in Stata Journal: Stata Journal Pages: 220-258 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000030 Abstract: How can social and health researchers study complex dynamic systems that function in nonlinear and even chaotic ways? Common methods, such as experiments and equation-based models, may be ill-suited to this task. To address the limitations of existing methods and offer nonparametric tools for characterizing and testing causality in nonlinear dynamic systems, we introduce the edm command in Stata. This command implements three key empirical dynamic modeling (EDM) methods for time series and panel data: 1) simplex projection, which characterizes the dimensionality of a system and the degree to which it appears to function deterministically; 2) S-maps, which quantify the degree of nonlinearity in a system; and 3) convergent cross-mapping, which offers a nonparametric approach to modeling causal effects. We illustrate these methods using simulated data on daily Chicago temperature and crime, showing an effect of temperature on crime but not the reverse. We conclude by discussing how EDM allows checking the assumptions of traditional model-based methods, such as residual autocorrelation tests, and we advocate for EDM because it does not assume linearity, stability, or equilibrium. Keywords: edm, empirical dynamic model, convergent cross-mapping, simplex projection, S-maps, causality, manifold, equilibrium File-URL: http://hdl.handle.net/10.1177/1536867X211000030 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0635/ Handle:RePEc:tsj:stataj:y:19:y:2019:i:1:p:220-258 Template-Type: ReDIF-Article 1.0 Author-Name: Christine Wells Author-Workplace-Name: University of California–Los Angeles Author-Email: crwells@ucla.edu Title: Review of Psychological Statistics and Psychometrics Using Stata, by Scott A. Baldwin Journal: Stata Journal Pages: 259-262 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000031 Abstract: In this article, I review Psychological Statistics and Psychometrics Using Stata, by Scott A. Baldwin (2019, Stata Press). Keywords: book review, psychometrics, regression, ANOVA, multilevel, confirmatory factor analysis, exploratory factor analysis, Stata File-URL: http://hdl.handle.net/10.1177/1536867X211000031 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/gn0085/ Handle:RePEc:tsj:stataj:y:19:y:2019:i:1:p:259-262 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 140: Shorter or fewer category labels with graph bar Journal: Stata Journal Pages: 263-271 Issue: 1 Volume: 21 Year: 2021 Month: March X-DOI: 10.1177/1536867X211000032 File-URL: http://hdl.handle.net/10.1177/1536867X211000032 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-1/gr0086/ Handle:RePEc:tsj:stataj:v:21:y:2021:i:1:p:263-271 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 272 Issue: 1 Volume: 21 Year: 2021 Month: March Abstract: Updates for previously published packages are provided. Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0173_2/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0259_1/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0370_1/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0533_2/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj21-1/st0611_1/ Note: Windows users should not attempt to download these files with a web browser. Handle:RePEc:tsj:stataj:v:21:y:2021:i:1:p:272