Template-Type: ReDIF-Article 1.0 Author-Name: Gregori Baetschmann Author-Workplace-Name: University of Bern Author-Email: gregori.baetschmann@soz.unibe.ch Author-Name: Alexander Ballantyne Author-Workplace-Name: University of Melbourne Author-Email: ballantynea@student.unimelb.edu.au Author-Name: Kevin E. Staub Author-Workplace-Name: University of Melbourne Author-Email: kevin.staub@unimelb.edu.au Author-Person: pst600 Author-Name: Rainer Winkelmann Author-Workplace-Name: University of Zurich Author-Email: rainer.winkelmann@econ.uzh.ch Author-Person: pwi6 Title: feologit: A new command for fitting fixed-effects ordered logit models Journal: Stata Journal Pages: 253-275 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20930984 Abstract: In this article, we describe how to fit panel-data ordered logit mod- els with fixed effects using the new community-contributed command feologit. Fixed-effects models are increasingly popular for estimating causal effects in the social sciences because they flexibly control for unobserved time-invariant hetero- geneity. The ordered logit model is the standard model for ordered dependent variables, and this command is the first in Stata specifically for this model with fixed effects. The command includes a choice between two estimators, the blow- up and cluster (BUC) estimator introduced in Baetschmann, Staub, and Winkel- mann (2015, Journal of the Royal Statistical Society, Series A 178: 685–703) and the BUC-τ estimator in Baetschmann (2012, Economics Letters 115: 416–418). Baetschmann, Staub, and Winkelmann (2015) showed that the BUC estimator has good properties and is almost as efficient as more complex estimators such as generalized method-of-moments and empirical likelihood estimators. The com- mand and model interpretations are illustrated with an analysis of the effect of parenthood on life satisfaction using data from the German Socio-Economic Panel. Copyright 2020 by StataCorp LP. Keywords: feologit, panel data, ordered dependent variables, logistic mod- els, fixed effects, blow-up and cluster estimator File-URL: http://www.stata-journal.com/article.html?article=st0596 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj20-2/st0596/ File-Format: text/html Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:253-275 Template-Type: ReDIF-Article 1.0 Author-Name: Javier Alejo Author-Workplace-Name: IECON-UdelaR Author-Email: javier.alejo@ccee.edu.uy Author-Person: pal181 Author-Name: Antonio F. Galvao Author-Workplace-Name: University of Arizona Author-Email: agalvao@email.arizona.edu Author-Name: Gabriel Montes-Rojas Author-Workplace-Name: CONICET-IIEP-UBA Author-Email: gabriel.montes@fce.uba.ar Author-Person: pmo380 Title: A practical generalized propensity-score estimator for quantile continuous treatment effects Journal: Stata Journal Pages: 276-296 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20930997 Abstract: In this article, we present a new command, qcte, that implements several methods for estimation and inference for quantile treatment-effects models with a continuous treatment. We propose a semiparametric two-step estimator, where the first step is based on a flexible Box–Cox model, as the default model of the command. We develop practical statistical inference procedures using boot- strap. We implement some simulations to show that the proposed methods perform well. Finally, we apply qcte to a survey of Massachusetts lottery winners to esti- mate the unconditional quantile effects of the prize amount, as a proxy of nonlabor income changes, on subsequent labor earnings from U.S. Social Security records. The empirical results reveal strong heterogeneity across unconditional quantiles. Copyright 2020 by StataCorp LP. Keywords: qcte, continuous treatment, quantile treatment effects, quantile regression File-URL: http://www.stata-journal.com/article.html?article=st0597 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj20-2/st0597/ File-Format: text/html Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:276-296 Template-Type: ReDIF-Article 1.0 Author-Name: Xavier D’Haultfoeuille Author-Workplace-Name: CREST-ENSAE Author-Email: xavier.dhaultfoeuille@ensae.fr Author-Person: pdh29 Author-Name: Arnaud Maurel Author-Workplace-Name: Duke University Author-Email: apm16@duke.edu Author-Person: pma1091 Author-Name: Xiaoyun Qiu Author-Workplace-Name: Northwestern University Author-Email: xiaoyun.qiu@u.northwestern.edu Author-Name: Yichong Zhang Author-Workplace-Name: Singapore Management University Author-Email: yczhang@smu.edu.sg Title: Estimating selection models without an instrument with Stata Journal: Stata Journal Pages: 297-308 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20930998 Abstract: In this article, we present the eqregsel command, which estimates and provides bootstrap inference for sample-selection models via extremal quantile regression. eqregsel estimates a semiparametric sample-selection model without an instrument or a large support regressor and outputs the point estimates of the homogeneous linear coefficients, their bootstrap standard errors, and the p-value for a specification test. Copyright 2020 by StataCorp LP. Keywords: eqregsel, sample-selection models, extremal quantile regressions File-URL: http://www.stata-journal.com/article.html?article=st0598 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj20-2/st0598/ File-Format: text/html Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:297-308 Template-Type: ReDIF-Article 1.0 Author-Name: Marshall A. Taylor Author-Workplace-Name: New Mexico State University Author-Email: mtaylor2@nmsu.edu Title: Visualization strategies for regression estimates with randomization inference Journal: Stata Journal Pages: 309-335 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20930999 Abstract: Coefficient plots are a popular tool for visualizing regression estimates. The appeal of these plots is that they visualize confidence intervals around the estimates and generally center the plot around zero, meaning that any estimate that crosses zero is statistically nonsignificant at least at the alpha level around which the confidence intervals are constructed. For models with statistical sig- nificance levels determined via randomization models of inference and for which there is no standard error or confidence intervals for the estimate itself, these plots appear less useful. In this article, I illustrate a variant of the coefficient plot for regression models with p-values constructed using permutation tests. These vi- sualizations plot each estimate’s p-value and its associated confidence interval in relation to a specified alpha level. These plots can help the analyst interpret and report the statistical and substantive significances of their models. I illustrate us- ing a nonprobability sample of activists and participants at a 1962 anticommunism school. Copyright 2020 by StataCorp LP. Keywords: permutation tests, visualization, coefficient plots, p-values File-URL: http://www.stata-journal.com/article.html?article=gr0083 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj20-2/gr0083/ File-Format: text/html Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:309-335 Template-Type: ReDIF-Article 1.0 Author-Name: E. F. Haghish Author-Workplace-Name: University of Göttingen Author-Email: haghish@med.uni-goettingen.de Title: Software documentation with markdoc 5.0 Journal: Stata Journal Pages: 336-362 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20931000 Abstract: markdoc is a general-purpose literate programming package for generating dynamic documents, dynamic presentation slides, Stata help files, and package vignettes in various formats. In this article, I introduce markdoc ver- sion 5.0, which performs independently of any third-party software, using the mini engine. The mini engine is a lightweight alternative to Pandoc (MacFarlane [2006, https://pandoc.org/]), completely written in Stata. I also propose a proce- dure for remodeling package documentation and data documentation in Stata and present a tutorial for generating help files, package vignettes, and GitHub Wiki documentation using markdoc. Copyright 2020 by StataCorp LP. Keywords: markdoc, mini, Pandoc, statistics software, software documentation, literate programming, social coding File-URL: http://www.stata-journal.com/article.html?article=pr0072 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj20-2/pr0072/ File-Format: text/html Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:336-362 Template-Type: ReDIF-Article 1.0 Author-Name: John A. Gallis Author-Workplace-Name: Duke University Author-Email: john.gallis@duke.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: xtgeebcv: A command for bias-corrected sandwich variance estimation for GEE analyses of cluster randomized trials Journal: Stata Journal Pages: 363-381 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20931001 Abstract: Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on indi- viduals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to be more similar than outcomes for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (for ex- ample, 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. Some bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, but none has yet been implemented in Stata. In this article, we describe several popular bias corrections to the robust sandwich variance. We then introduce our newly created command, xtgeebcv, which will allow Stata users to easily apply finite-sample corrections to standard errors obtained from GEE mod- els. We then provide examples to demonstrate the use of xtgeebcv. Finally, we discuss suggestions about which finite-sample corrections to use in which situations and consider areas of future research that may improve xtgeebcv. Copyright 2020 by StataCorp LP. Keywords: xtgeebcv, cluster randomized trials, bias-corrected variances, sandwich variance, generalized estimating equations, finite-sample correction File-URL: http://www.stata-journal.com/article.html?article=st0599 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj20-2/st0599/ File-Format: text/html Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:363-381 Template-Type: ReDIF-Article 1.0 Author-Name: Young Jun Lee Author-Workplace-Name: University of Copenhagen Author-Email: yjl@econ.ku.dk Author-Name: Daniel Wilhelm Author-Workplace-Name: University College London, CeMMAP Author-Email: d.wilhelm@ucl.ac.uk Author-Person: pwi343 Title: Testing for the presence of measurement error in Stata Journal: Stata Journal Pages: 382-404 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20931002 Abstract: In this article, we describe how to test for the presence of measure- ment error in explanatory variables. First, we discuss the test of such hypotheses in parametric models such as linear regressions and then introduce a new com- mand, dgmtest, for a nonparametric test proposed in Wilhelm (2018, Working Paper CWP45/18, Centre for Microdata Methods and Practice, Institute for Fis- cal Studies). To illustrate the new command, we provide Monte Carlo simulations and an empirical application to testing for measurement error in administrative earnings data. Keywords: dgmtest, nonparametric test, measurement error, measurement error bias File-URL: http://www.stata-journal.com/article.html?article=st0600 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj20-2/st0600/ File-Format: text/html Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:382-404 Template-Type: ReDIF-Article 1.0 Author-Name: Hong Il Yoo Author-WorkPlace-Name: Durham University Business School Author-Email: h.i.yoo@durham.ac.uk Author-Person: pyo103 Title: lclogit2: An enhanced command to fit latent class conditional logit models Journal: Stata Journal Pages: 405-425 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20931003 Abstract: In this article, I describe the lclogit2 command, an enhanced version of lclogit (Pacifico and Yoo, 2013, Stata Journal 13: 625–639). Like its predeces- sor, lclogit2 uses the expectation-maximization algorithm to fit latent class conditional logit (LCL) models. But it executes the expectation-maximization algorithm’s core algebraic operations in Mata, so it runs considerably faster as a result. It also allows linear constraints on parameters to be imposed more conveniently and flexibly. It comes with the parallel command lclogitml2, a new stand-alone command that uses gradient-based algorithms to fit LCL models. Both lclogit2 and lclogitml2 are supported by a new postestimation command, lclogitwtp2, that evaluates willingness-to-pay measures implied by fitted LCL models. Keywords: lclogit2, lclogitml2, lclogitpr2, lclogitcov2, lclogitwtp2, latent class model, conditional logit, expectation-maximization algorithm, lclogit, fmm, finite mixture, mixlogit, mixed logit, willingness to pay File-URL: http://www.stata-journal.com/article.html?article=st0601 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj20-2/st0601/ File-Format: text/html Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:405-425 Template-Type: ReDIF-Article 1.0 Author-Name: Muhammad Asali Author-WorkPlace-Name: Tbilisi State University Author-Email: muhammad.asali@gmail.com Author-Person: pas130 Title: vgets: A command to estimate general-to-specific VARs, Granger causality, steady-state effects, and cumulative impulse–responses Journal: Stata Journal Pages: 426-434 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20931004 Abstract: Vector autoregression (VAR) estimation is a vital tool in economic studies. VARs, however, can be dimensionally cumbersome and overparameter- ized. The vgets command allows for a general-to-specific estimation of VARs— overcoming the potential overparameterization—and provides tests for Granger causality, estimates of the long-run effects, and the cumulative impulse–response of each variable in the system; it also offers diagnostics that facilitate a genuine-causality interpretation of the Granger causality tests. Keywords: vgets, general-to-specific vector autoregressions, Granger causality, steady-state effects, cumulative impulse–responses File-URL: http://www.stata-journal.com/article.html?article=st0602 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj20-2/st0602/ File-Format: text/html Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:426-434 Template-Type: ReDIF-Article 1.0 Author-Name: Zizhong Yan Author-WorkPlace-Name: Jinan University Author-Email: helloyzz@gmail.com Author-Person: pya369 Author-Name: Wiji Arulampalam Author-WorkPlace-Name: University of Warwick Author-Email: wiji.arulampalam@warwick.ac.uk Author-Person: par8 Author-Name: Valentina Corradi Author-WorkPlace-Name: University of Surrey Author-Email: v.corradi@surrey.ac.uk Author-Person: pco129 Author-Name: Daniel Gutknecht Author-WorkPlace-Name: Goethe University Frankfurt Author-Email: daniel.gutknecht@gmx.de Author-Person: pgu410 Title: heap: A command for fitting discrete outcome variable models in the presence of heaping at known points Journal: Stata Journal Pages: 435-467 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20931005 Abstract: Self-reported survey data are often plagued by the presence of heaping. Accounting for this measurement error is crucial for the identification and consistent estimation of the underlying model (parameters) from such data. In this article, we introduce two commands. The first command, heapmph, estimates the parameters of a discrete-time mixed proportional hazard model with gamma- unobserved heterogeneity, allowing for fixed and individual-specific censoring and different-sized heap points. The second command, heapop, extends the frame- work to ordered choice outcomes, subject to heaping. We also provide suitable specification tests. Keywords: heapmph, heapop, discrete-time duration model, mixed proportional hazards model, ordered choice model, heaping, measurement error File-URL: http://www.stata-journal.com/article.html?article=st0603 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj20-2/st0603/ File-Format: text/html Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:435-467 Template-Type: ReDIF-Article 1.0 Author-Name: Koen Jochmans Author-WorkPlace-Name: University of Cambridge Author-Email: kj345@cam.ac.uk Author-Person: pjo240 Author-Name: Vincenzo Verardi Author-WorkPlace-Name: Université de Namur Author-Email: vverardi@unamur.be Author-Person: pve73 Title: Fitting exponential regression models with two-way fixed effects Journal: Stata Journal Pages: 468-480 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20931006 Abstract: In this article, we introduce the commands twexp and twgravity, which implement the estimators developed in Jochmans (2017, Review of Economics and Statistics 99: 478–485) for exponential regression models with two-way fixed effects. twexp is applicable to generic n × m panel data. twgravity is written for the special case where the dataset is a cross-section on dyadic interactions between n agents. A prime example is cross-sectional bilateral trade data, where the model of interest is a gravity equation with importer and exporter effects. Both twexp and twgravity can deal with data where n and m are large, that is, where there are many fixed effects. These commands use Mata and are fast to execute. Keywords: twexp, twgravity, exponential regression, gravity model, panel data, two-way fixed effects File-URL: http://www.stata-journal.com/article.html?article=st0604 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj20-2/st0604/ File-Format: text/html Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:468-480 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: More ways for rowwise Journal: Stata Journal Pages: 481-488 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20931007 Abstract: A previous column (Cox, 2009, Stata Journal 9: 137–157) gave a review of methods for working rowwise in Stata. Here rows means observations in a dataset, and the concern is calculations in each observation with a bundle of variables. For example, a row mean variable can be generated as the mean of some numeric variables in each observation. This column is an update. It is briefly flagged that official Stata now has rowmedian() and rowpctile() functions for egen. The main focus is on returning which variable or variables are equal to the maximum or minimum in a row. The twist that requires care is that two or more variables can tie for minimum or maximum. That may entail a decision on what is to be recorded, such as all of them or just the first or last occurrence of an extreme. Keywords: rows, functions, minimum, maximum, median, loops, egen File-URL: http://www.stata-journal.com/article.html?article=pr0046_1 File-Function: link to article purchase File-URL: http://www.stata-journal.com/software/sj20-2/pr0046_1/ File-Format: text/html Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:481-488 Template-Type: ReDIF-Article 1.0 Author-Name: Andrew Musau Author-WorkPlace-Name: INN University Author-Email: andrew.musau@inn.no Author-Person: pmu556 Title: Stata tip 136: Between-group comparisons in a scatterplot with weighted markers Journal: Stata Journal Pages: 489-492 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20931008 File-URL: http://www.stata-journal.com/article.html?article=gr0084 File-Function: link to article purchase Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:489-492 Template-Type: ReDIF-Article 1.0 Author-Name: Demetris Christodoulou Author-WorkPlace-Name: University of Sydney Author-Email: demetris.christodoulou@sydney.edu.au Author-Person: pch1698 Title: Stata tip 137: Interpreting constraints on slopes of rank-deficient design matrices Journal: Stata Journal Pages: 493-498 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20931027 File-URL: http://www.stata-journal.com/article.html?article=st0605 File-Function: link to article purchase Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:493-498 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 138: Local macros have local scope Journal: Stata Journal Pages: 499-503 Issue: 2 Volume: 20 Year: 2020 Month: June X-DOI: 10.1177/1536867X20931028 Abstract: Keywords: File-URL: http://www.stata-journal.com/article.html?article=dm0102 File-Function: link to article purchase Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:499-503 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 504 Issue: 2 Volume: 20 Year: 2020 Month: June Abstract: Updates for previously published packages are provided. File-URL: http://www.stata-journal.com/software/sj20-2/dm0048_4/ File-Format: text/html File-URL: http://www.stata-journal.com/software/sj20-2/st0521_1/ File-Format: text/html File-URL: http://www.stata-journal.com/software/sj20-2/st0578_1/ File-Format: text/html File-URL: http://www.stata-journal.com/software/sj20-2/st0581_1/ File-Format: text/html Note: Windows users should not attempt to download these files with a web browser. Handle:RePEc:tsj:stataj:v:20:y:2019:i:2:p:504