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: Peter Anthony Lachenbruch (1937–2021) Journal: Stata Journal Pages: 241-242 Issue: 2 Volume: 22 Year: 2022 Month: June File-URL: http://www.stata-journal.com/article.html?article=gn0091 File-Function: link to article purchase DOI: 10.1177/1536867X221106359 Handle: RePEc:tsj:stataj:v:22:y:2022:i:2:p:241-242 Template-Type: ReDIF-Article 1.0 Author-Name: Bartosz Kondratek Author-Workplace-Name: Educational Research Institute Author-Email: b.kondratek@ibe.edu.pl Title: uirt: A command for unidimensional IRT modeling Journal: Stata Journal Pages: 243-268 Issue: 2 Volume: 22 Year: 2022 Month: June Abstract: In this article, I introduce the uirt command, which allows one to estimate parameters of a variety of unidimensional item response theory models (two-parameter logistic model, three-parameter logistic model, graded response model, partial credit model, and generalized partial credit model). uirt has ex- tended item-fit analysis capabilities, features multigroup modeling, allows testing for differential item functioning, and provides tools for generating plausible val- ues with a latent regression conditioning model. I provide examples to illustrate cases where uirt can be especially useful in conducting analyses within the item response theory approach. Keywords: uirt, uirt_theta, uirt_icc, uirt_dif, uirt_chi2w, uirt_sx2, uirt_esf, uirt_inf, item response theory, item-fit, unidimensional item response theory models, differential item functioning, partial credit model, plausible values File-URL: http://www.stata-journal.com/article.html?article=st0670 File-Function: link to article purchase DOI: 10.1177/1536867X221106368 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/st0670/ Handle: RePEc:tsj:stataj:v:22:y:2022:i:2:p:243-268 Template-Type: ReDIF-Article 1.0 Author-Name: Ali Kagalwala Author-Workplace-Name: Texas A&M University Author-Email: alikagalwala@tamu.edu Title: kpsstest: A command that implements the Kwiatkowski, Phillips, Schmidt, and Shin test with sample-specific critical values and reports p-values Journal: Stata Journal Pages: 269-292 Issue: 2 Volume: 22 Year: 2022 Month: June Abstract: Commonly used unit-root tests in time-series analysis—such as the Dickey–Fuller and Phillips–Perron tests—use a null hypothesis that the series con- tains a unit root. Such tests have low power against the alternative—when a time series is near integrated or highly autoregressive—implying that they do poorly in distinguishing such a series from having a unit root. Kwiatkowski et al. (1992, Jour- nal of Econometrics 54: 159–178) introduced the Kwiatkowski, Phillips, Schmidt, and Shin test, in which the null hypothesis is that the series is stationary, to deal with this problem. One shortcoming of the presently available Kwiatkowski, Phillips, Schmidt, and Shin test in Stata is that it uses asymptotic critical values regardless of the sample size. This poses a problem in that researchers—especially social scientists—are often presented with short time series. I introduce kpsstest, a command that extends the previous implementation by including an option for a zero-mean-stationary null hypothesis, generating sample- and test-specific critical values, and reporting appropriate p-values. Keywords: kpsstest, KPSS, GKPSS, unit-root tests, stationary null hypothesis, time series File-URL: http://www.stata-journal.com/article.html?article=st0671 File-Function: link to article purchase DOI: 10.1177/1536867X221106371 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/st0671/ Handle: RePEc:tsj:stataj:v:22:y:2022:i:2:p:269-292 Template-Type: ReDIF-Article 1.0 Author-Name: Daniele Spinelli Author-Workplace-Name: University of Milan–Bicocca Author-Email: daniele.spinelli@unimib.it Author-Person: psp173 Title: Fitting spatial autoregressive logit and probit models using Stata: The spatbinary command Journal: Stata Journal Pages: 293-318 Issue: 2 Volume: 22 Year: 2022 Month: June Abstract: Starting from version 15, Stata allows users to manage data and fit regressions accounting for spatial relationships through the sp commands. Spatial regressions can be estimated using the spregress, spxtregress, and spivregress commands. These commands allow users to fit spatial autoregressive models in cross-sectional and panel data. However, they are designed to estimate regressions with continuous dependent variables. Although binary spatial regressions are im- portant in applied econometrics, they cannot be estimated in Stata. Therefore, I introduce spatbinary, a Stata command that allows users to fit spatial logit and probit models. Keywords: spatbinary, spatbinary_impact, postestimation, spatial logit, spatial probit, spatial autoregressive models, marginal effects File-URL: http://www.stata-journal.com/article.html?article=st0672 File-Function: link to article purchase DOI: 10.1177/1536867X221106373 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/st0672/ Handle: RePEc:tsj:stataj:v:22:y:2022:i:2:p:292-318 Template-Type: ReDIF-Article 1.0 Author-Name: Marcos Demetry Author-Workplace-Name: Linnaeus University Author-Email: marcos.demetry@lnu.se Author-Person: pde1285 Author-Name: Per Hjertstrand Author-Workplace-Name: Research Institute of Industrial Economics Author-Email: per.hjertstrand@ifn.se Author-Person: phj4 Author-Name: Matthew Polisson Author-Workplace-Name: University of Bristol Author-Email: matthew.polisson@bristol.ac.uk Author-Person: ppo336 Title: Testing axioms of revealed preference in Stata Journal: Stata Journal Pages: 319-343 Issue: 2 Volume: 22 Year: 2022 Month: June Abstract: The revealed preference approach in economics is central to the empirical analysis of consumer behavior. In this article, we introduce the commands checkax, aei, and powerps as a bundle within the package rpaxioms. The first command allows a user to test whether consumer expenditure data satisfy several revealed preference axioms; the second command calculates measures of good- ness of fit when the data violate these axioms; and the third command calculates power against uniformly random behavior as well as predictive success for each axiom. We illustrate the commands using individual-level experimental data and household-level aggregate consumption data. Keywords: rpaxioms, checkax, aei, powerps, revealed preference, generalized axiom of revealed preference, Afriat efficiency index, power, predictive success File-URL: http://www.stata-journal.com/article.html?article=st0673 File-Function: link to article purchase DOI: 10.1177/1536867X221106374 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/st0673/ Handle: RePEc:tsj:stataj:v:22:y:2022:i:2:p:319-343 Template-Type: ReDIF-Article 1.0 Author-Name: Yuya Sasaki Author-Workplace-Name: Vanderbilt University Author-Email: yuya.sasaki@vanderbilt.edu Author-Person: psa1792 Author-Name: Takuya Ura Author-Workplace-Name: University of California, Davis Author-Email: takura@ucdavis.edu Title: Average treatment effect estimates robust to the “limited overlap” problem: robustate Journal: Stata Journal Pages: 344-354 Issue: 2 Volume: 22 Year: 2022 Month: June Abstract: We introduce a new command, robustate, that executes the inverse-probability weighting estimation and inference for the average treatment effect with robustness against limited overlap (that is, weak satisfaction of the common support condition). This command produces estimates, standard errors, p-values, and confidence intervals for the average treatment effect. The utility of the com- mand is demonstrated with both simulated and real data of right heart catheteri- zation. These illustrations show that the proposed estimator implemented by the robustate command indeed exhibits more robustness against limited overlap than the traditional inverse-probability weighting estimator. The main method of the command is proposed in Sasaki and Ura (2022, Econometric Theory 38: 66–112). Keywords: robustate, average treatment effect, bias correction, common support, inverse-probability weighting, limited overlap, robustness, trimming File-URL: http://www.stata-journal.com/article.html?article=st0674 File-Function: link to article purchase DOI: 10.1177/1536867X221106402 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/st0674/ Handle: RePEc:tsj:stataj:v:22:y:2022:i:2:p:344-354 Template-Type: ReDIF-Article 1.0 Author-Name: Christopher F Baum Author-Workplace-Name: Boston College Author-Workplace-Name: DIW Berlin Author-Email: baum@bc.edu Author-Person: pba1 Author-Name: Stan Hurn Author-Workplace-Name: Queensland University of Technology Author-Email: s.hurn@qut.edu.au Author-Person: phu111 Author-Name: Kenneth Lindsay Author-Workplace-Name: University of Glasgow Author-Email: kenneth.lindsay@glasgow.ac.uk Author-Name: Jesús Otero Author-Workplace-Name: Universidad del Rosario Author-Email: jesus.otero@urosario.edu.co Author-Person: pot11 Title: Testing for time-varying Granger causality Journal: Stata Journal Pages: 355-378 Issue: 2 Volume: 22 Year: 2022 Month: June Abstract: The concept of Granger causality is an important tool in applied macroeconomics. Recently, recursive econometric methods have been developed to analyze the temporal stability of Granger-causal relationships. This article offers an implementation of these recursive procedures in Stata. An empirical example illustrates their use in analyzing the temporal stability of Granger causality among key U.S. macroeconomic series. Keywords: tvgc, Granger causality, time variation, temporal stability, datestamping File-URL: http://www.stata-journal.com/article.html?article=st0675 File-Function: link to article purchase DOI: 10.1177/1536867X221106403 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/st0675/ Handle:RePEc:tsj:stataj:v:22:y:2022:i:2:p:355-378 Template-Type: ReDIF-Article 1.0 Author-Name: David M. Kaplan Author-Workplace-Name: University of Missouri Author-Email: kaplandm@missouri.edu Author-Person: pka649 Title: Smoothed instrumental variables quantile regression Journal: Stata Journal Pages: 379-403 Issue: 2 Volume: 22 Year: 2022 Month: June Abstract: In this article, I introduce the sivqr command, which estimates the coefficients of the instrumental variables quantile regression model introduced by Chernozhukov and Hansen (2005, Econometrica 73: 245–261). The sivqr com- mand offers several advantages over the existing ivqreg and ivqreg2 commands for estimating this instrumental variables quantile regression model, which com- plements the alternative “triangular model” behind cqiv and the “local quan- tile treatment effect” model of ivqte. Computationally, sivqr implements the smoothed estimator of Kaplan and Sun (2017, Econometric Theory 33: 105–157), who show that smoothing improves both computation time and statistical accu- racy. Standard errors are computed analytically or by Bayesian bootstrap; for nonindependent and identically distributed sampling, sivqr is compatible with bootstrap. I discuss syntax and the underlying methodology, and I compare sivqr with other commands in an example. Keywords: sivqr, endogeneity, instrumental variables, quantile regression File-URL: http://www.stata-journal.com/article.html?article=st0676 File-Function: link to article purchase DOI: 10.1177/1536867X221106404 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/st0676/ Handle: RePEc:tsj:stataj:v:22:y:2022:i:2:p:379-403 Template-Type: ReDIF-Article 1.0 Author-Name: Patricio Troncoso Author-Workplace-Name: Heriot-Watt University Author-Email: p.troncoso@hw.ac.uk Author-Name: Ana Morales-Gómez Author-Workplace-Name: University of Edinburgh Author-Email: Ana.Morales@ed.ac.uk Title: Estimating the complier average causal effect via a latent class approach using gsem Journal: Stata Journal Pages: 404-415 Issue: 2 Volume: 22 Year: 2022 Month: June Abstract: In randomized controlled trials, intention-to-treat analysis is custom- arily used to estimate the effect of the trial. However, in the presence of noncom- pliance, this can often lead to biased estimates because intention-to-treat analysis completely ignores varying levels of actual treatment received. This is a known is- sue that can be overcome by adopting the complier average causal effect approach, which estimates the effect the trial had on the individuals who complied with the protocol. When compliance is unobserved in the control group, the complier av- erage causal effect estimate can be obtained via a latent class specification using the gsem command. Keywords: gsem, complier average causal effect, randomized control trial, compliance, adherence, latent class modeling, mixture modeling File-URL: http://www.stata-journal.com/article.html?article=st0677 File-Function: link to article purchase DOI: 10.1177/1536867X221106416 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/st0677/ Handle: RePEc:tsj:stataj:v:22:y:2022:i:2:p:404-415 Template-Type: ReDIF-Article 1.0 Author-Name: Niels Henrik Bruun Author-Workplace-Name: Aalborg University Hospital Author-Email: nbru@rn.dk Author-Person: pbr821 Title: Interactively building table reports with basetable Journal: Stata Journal Pages: 416-429 Issue: 2 Volume: 22 Year: 2022 Month: June Abstract: In statistical work, it is essential to have an overview of the data used. In, for example, biomedical articles, a standardized way of reporting summaries of continuous and categorical variables is “table 1”. This standardized way of reporting can be useful in most cases of statistical work. The basetable command is a flexible and straightforward way to build and format such table reports. The final reports are easy to style into Stata Markup and Control Language, comma- separated values, HyperText Markup Language, LATEX or TEX, or Markdown and, for example, save into a file specified by the using modifier. Also, it is possible to export the reports created by basetable into Excel worksheets. Because of the General Data Protection Regulation, it has become necessary to blur information on individuals when making reports; in basetable, there are options to blur both categorical and continuous data. Keywords: basetable, table reports, table 1 File-URL: http://www.stata-journal.com/article.html?article=st0678 File-Function: link to article purchase DOI: 10.1177/1536867X221106417 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/st0678/ Handle: RePEc:tsj:stataj:v:22:y:2022:i:2:p:416-429 Template-Type: ReDIF-Article 1.0 Author-Name: Matteo Pinna Author-Workplace-Name: ETH Zürich Author-Email: matteo.pinna@gess.ethz.ch Author-Person: ppi504 Title: Binned scatterplots with marginal histograms: binscatterhist Journal: Stata Journal Pages: 430-445 Issue: 2 Volume: 22 Year: 2022 Month: June Abstract: I introduce binscatterhist, a command that extends the functionality of the popular binscatter command (Stepner, 2013, Statistical Software Compo- nents S457709, Department of Economics, Boston College). binscatter allows researchers to summarize the relationship between two variables in an informative and versatile way by collapsing scattered points into bins. However, information about the variables’ frequencies gets lost in the process. binscatterhist solves this issue by allowing the user to further enrich the graphs by plotting the variables’ underlying distribution. The binscatterhist command includes options for dif- ferent regression methods, including reghdfe (Correia, 2014, Statistical Software Components S457874, Department of Economics, Boston College) and areg, and robust and clustered standard errors, with automatic reporting of estimation results and sample size. Keywords: binscatterhist, binscatter, histogram, scatter, ggscatterhist, scatterhist, binned scatterplots, marginal histograms File-URL: http://www.stata-journal.com/article.html?article=gr0091 File-Function: link to article purchase DOI: 10.1177/1536867X221106418 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/gr0091/ Handle: RePEc:tsj:stataj:v:22:y:2022:i:2:p:430-445 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: The largest five-—A tale of tail values Journal: Stata Journal Pages: 446-459 Issue: 2 Volume: 22 Year: 2022 Month: June Abstract: How do you work with the largest five, or smallest five, or any other fixed number of values in a tail of a distribution? In this column, I give examples of problems and code for basic calculations as a prelude to graphics, tables, and more detailed analysis. The main illustration is analysis of concentration among firms or companies, with wider discussion mentioning hydrology, climatology, cryptog- raphy, and ecology. The examples allow a tutorial covering sorting and ranking and using if and in to select observations, by: as a framework for groupwise cal- culations, indicator variables as a mode of selection, and egen as a Swiss Army knife with many handy functions. Keywords: by:, egen, if, in, ranking, sorting, tails, concentration, diversity, inequality File-URL: http://www.stata-journal.com/article.html?article=dm0108 File-Function: link to article purchase DOI: 10.1177/1536867X221106436 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/dm0108/ Handle:RePEc:tsj:stataj:v:22:y:2022:i:2:p:446-459 Template-Type: ReDIF-Article 1.0 Author-Name: Milena Falcaro Author-Workplace-Name: King’s College London Author-Email: milena.falcaro@kcl.ac.uk Author-Name: Roger B. Newson Author-Workplace-Name: King’s College London Author-Email: roger.newson@kcl.ac.uk Author-Person: pne37 Author-Name: Peter Sasieni Author-Workplace-Name: King’s College London Author-Email: peter.sasieni@kcl.ac.uk Title: Stata tip 146: Using margins after a Poisson regression model to estimate the number of events prevented by an intervention Journal: Stata Journal Pages: 460-464 Issue: 2 Volume: 22 Year: 2022 Month: June File-URL: http://www.stata-journal.com/article.html?article=st0679 File-Function: link to article purchase DOI: 10.1177/1536867X221106437 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/st0679/ Handle: RePEc:tsj:stataj:v:22:y:2022:i:2:p:460-464 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: Erratum: Speaking Stata 145: Numbering weeks within months Journal: Stata Journal Pages: 465-466 Issue: 2 Volume: 22 Year: 2022 Month: June File-URL: http://www.stata-journal.com/article.html?article=dm0107_1 File-Function: link to article purchase DOI: 10.1177/1536867X221106438 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/dm0107_1/ Handle:RePEc:tsj:stataj:v:22:y:2022:i:2:p:465-466 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 467 Issue: 2 Volume: 22 Year: 2022 Month: June Abstract: Updates for previously published packages are provided. Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/gr0066_3/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj22-2/st0507_1/ Handle:RePEc:tsj:stataj:v:22:y:2022:i:2:p:467