Template-Type: ReDIF-Article 1.0 Author-Name: Stephen P. Jenkins Author-Workplace-Name: London School of Economics and Political Science Author-Email: s.jenkins@lse.ac.uk Author-Person: pje7 Title: Comparing distributions of ordinal data Journal: Stata Journal Pages: 505-531 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: To compare distributions of ordinal data such as individuals’ responses on Likert-type scale variables summarizing subjective well-being, we should not apply the toolbox of methods developed for cardinal variables such as income. Instead, we should use an analogous toolbox that accounts for the ordinal nature of the responses. In this article, I review these methods and introduce a new command, ineqord, for undertaking distributional comparisons. As the empirical illustrations demonstrate, ineqord can be used for dominance checks as well as for estimation of indices of polarization and inequality. Keywords: ineqord, inequality, ordinal data, subjective well-being, life satisfaction, Annual Population Survey File-URL: http://www.stata-journal.com/article.html?article=st0606 File-Function: link to article purchase X-DOI: 10.1177/1536867X20953565 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0606/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:505-531 Template-Type: ReDIF-Article 1.0 Author-Name: Eduardo Fé Author-Workplace-Name: University of Manchester Author-Email: eduardo.fe@manchester.ac.uk Author-Name: Richard Hofler Author-Workplace-Name: University of Central Florida Author-Email: richard.hofler@ucf.edu Title: sfcount: Command for count-data stochastic frontiers and underreported and overreported counts Journal: Stata Journal Pages: 532-547 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: In this article, we introduce a new command, sfcount, to fit count-data stochastic frontier models. Although originally designed to estimate production and production-cost functions, this new command can be used to estimate mean regression functions when count data are suspected to be underreported or over- reported. Keywords: sfcount, stochastic frontier, underreporting, overreporting, low- discrepancy series, mixed Poisson distribution, Poisson log-half-normal model, maximum simulated likelihood File-URL: http://www.stata-journal.com/article.html?article=st0607 File-Function: link to article purchase X-DOI: 10.1177/1536867X20953566 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0607/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:532-547 Template-Type: ReDIF-Article 1.0 Author-Name: David J. Miller Author-Workplace-Name: U.S. Environmental Protection Agency Author-Email: miller.davidj@epa.gov Author-Name: James T. Nguyen Author-Workplace-Name: U.S. Environmental Protection Agency Author-Email: nguyen.james@epa.gov Author-Name: Matteo Bottai Author-Workplace-Name: Institute of Environmental Medicine, Karolinska Institutet Author-Email: matteo.bottai@ki.se Title: emagnification: A tool for estimating effect-size magnification and performing design calculations in epidemiological studies Journal: Stata Journal Pages: 548-564 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: Artificial effect-size magnification (ESM) may occur in underpowered studies, where effects are reported only because they or their associated p-values have passed some threshold. Ioannidis (2008, Epidemiology 19: 640–648) and Gel- man and Carlin (2014, Perspectives on Psychological Science 9: 641–651) have suggested that the plausibility of findings for a specific study can be evaluated by computation of ESM, which requires statistical simulation. In this article, we present a new command called emagnification that allows straightforward im- plementation of such simulations in Stata. The commands automate these sim- ulations for epidemiological studies and enable the user to assess ESM routinely for published studies using user-selected, study-specific inputs that are commonly reported in published literature. The intention of the command is to allow a wider community to use ESMs as a tool for evaluating the reliability of reported effect sizes and to put an observed statistically significant effect size into a fuller context with respect to potential implications for study conclusions. Keywords:emagnification proportion, emagnification rate, inflation, mag- nification, p-value, type M error, effect-size magnification, winners curse File-URL: http://www.stata-journal.com/article.html?article=st0608 File-Function: link to article purchase X-DOI: 10.1177/1536867X20953567 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0608/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:548-564 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 Title: Local Whittle estimation of the long-memory parameter Journal: Stata Journal Pages: 565-583 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: In this article, we describe and implement the local Whittle and exact local Whittle estimators of the order of fractional integration of a time series. Keywords: whittle, Whittle estimator, long memory, fractional integration File-URL: http://www.stata-journal.com/article.html?article=st0609 File-Function: link to article purchase X-DOI: 10.1177/1536867X20953569 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0609/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:565-583 Template-Type: ReDIF-Article 1.0 Author-Name: Yoo Sun Jung Author-Email: yoosunjung@tamu.edu Author-WorkPlace-Name: University of California–San Diego Author-Name: Flávio D. S. Souza Author-Email: fsouza@tamu.edu Author-WorkPlace-Name: Texas A&M University Author-Name: Andrew Q. Philips Author-Email: andrew.philips@colorado.edu Author-WorkPlace-Name: University of Colorado Boulder Author-Name: Amanda Rutherford Author-Email: aruther@indiana.edu Author-WorkPlace-Name: Indiana University Author-Name: Guy D. Whitten Author-Email: g-whitten@pols.tamu.edu Author-WorkPlace-Name: Texas A&M University Title: A command to estimate and interpret models of dynamic compositional dependent variables: New features for dynsimpie Journal: Stata Journal Pages: 584-603 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: Philips, Rutherford, and Whitten (2016, Stata Journal 16: 662–677) introduced dynsimpie, a command to examine dynamic compositional dependent variables. In this article, we present an update to dynsimpie and three new ado- files: cfbplot, effectsplot, and dynsimpiecoef. These updates greatly enhance the range of models that can be estimated and the ways in which model results can now be presented. The command dynsimpie has been updated so that users can obtain both prediction plots and change-from-baseline plots using postestimation commands. With the new command dynsimpiecoef, various types of coefficient plots can also be obtained. We illustrate these improvements using monthly data on support for political parties in the United Kingdom. Keywords: dynsimpie, cfbplot, effectsplot, dynsimpiecoef, time series, seemingly unrelated regression, cointegration, dynamic modeling, dynamic com- position, error correction, lagged dependent variable File-URL: http://www.stata-journal.com/article.html?article=st0448_1 File-Function: link to article purchase X-DOI: 10.1177/1536867X20953570 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0448_1/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:584-603 Template-Type: ReDIF-Article 1.0 Author-Name: Carlos M. Urzúa Author-Workplace-Name: Tecnológico de Monterrey Author-Email: curzua@tec.mx Title: A simple test for power-law behavior Journal: Stata Journal Pages: 604-612 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: In this article, I propose a new test for power-law behavior. The statistical test, pwlaw, is locally optimal if the possible alternative distributions are contained in the Pareto type (IV) family. After deriving the test, I examine four classical datasets: the frequency of unique words in an English text (Moby Dick); the human populations of U.S. cities; the frequency of U.S. family names; and the peak gamma-ray intensity of solar flares. I show that in the first case there is no indication of any power-law behavior and that in the second and fourth cases there is evidence in that regard. Keywords: pwlaw, power-law distribution, Pareto law, Zipf’s law, Pareto (IV) distribution File-URL: http://www.stata-journal.com/article.html?article=st0610 File-Function: link to article purchase X-DOI: 10.1177/1536867X20953571 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0610/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:604-612 Template-Type: ReDIF-Article 1.0 Author-Name: Christian Dippel Author-Email: christian.dippel@anderson.ucla.edu Author-WorkPlace-Name: UCLA Anderson School of Management Author-Name: Andreas Ferrara Author-Email: a.ferrara@pitt.edu Author-WorkPlace-Name: University of Pittsburgh Author-Person: pfe513 Author-Name: Stephan Heblich Author-Email: stephan.heblich@utoronto.ca Author-WorkPlace-Name: University of Toronto Author-Person: phe224 Title: Causal mediation analysis in instrumental-variables regressions Journal: Stata Journal Pages: 613-626 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: In this article, we describe the use of ivmediate, a new command to estimate causal mediation effects in instrumental-variables settings using the framework developed by Dippel et al. (2020, unpublished manuscript). ivmediate allows estimation of a treatment effect and the share of this effect that can be attributed to a mediator variable. While both treatment and mediator can be potentially endogenous, a single instrument suffices to identify both the causal treatment and the mediation effects. Keywords: ivmediate, causal mediation analysis, treatment effects, instrumental variables File-URL: http://www.stata-journal.com/article.html?article=st0611 File-Function: link to article purchase X-DOI: 10.1177/1536867X20953572 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0611/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:613-626 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 switching regression model and treatment effects of count-data outcome Journal: Stata Journal Pages: 627-646 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: In this article, I describe the escount command, which implements the estimation of an endogenous switching model with count-data outcomes, where a potential outcome differs across two alternate treatment statuses. escount al- lows for either a Poisson or a negative binomial regression model with lognormal latent heterogeneity. After estimating the parameters of the switching regression model, one can estimate various treatment effects with the command teescount. I also describe the command lncount, which fits the Poisson or negative binomial regression model with lognormal latent heterogeneity. Keywords: escount, lncount, teescount, self-selection, count data, treatment effects File-URL: http://www.stata-journal.com/article.html?article=st0612 File-Function: link to article purchase X-DOI: 10.1177/1536867X20953573 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0612/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:627-646 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: Smooth varying-coefficient models in Stata Journal: Stata Journal Pages: 647-679 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: Nonparametric regressions are powerful statistical tools that can be used to model relationships between dependent and independent variables with minimal assumptions on the underlying functional forms. Despite their poten- tial benefits, these models have two weaknesses: The added flexibility creates a curse of dimensionality, and procedures available for model selection, like cross- validation, have a high computational cost in samples with even moderate sizes. An alternative to fully nonparametric models is semiparametric models that com- bine the flexibility of nonparametric regressions with the structure of standard models. In this article, I describe the estimation of a particular type of semipara- metric model known as the smooth varying-coefficient model (Hastie and Tibshi- rani, 1993, Journal of the Royal Statistical Society, Series B 55: 757–796), based on kernel regression methods, using a new set of commands within vc pack. These commands aim to facilitate bandwidth selection and model estimation as well as create visualizations of the results. Keywords: vc pack, vc bw, vc bwalt, vc reg, vc bsreg, vc preg, vc predict, vc test, vc graph, smooth varying-coefficient models, kernel regression, cross-vali- dation, semiparametric estimations File-URL: http://www.stata-journal.com/article.html?article=st0613 File-Function: link to article purchase X-DOI: 10.1177/1536867X20953574 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0613/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:647-679 Template-Type: ReDIF-Article 1.0 Author-Name: Evangelos Kontopantelis Author-Workplace-Name: University of Manchester Author-Email: e.kontopantelis@manchester.ac.uk Author-Name: David Reeves Author-Workplace-Name: University of Manchester Author-Email: david.reeves@manchester.ac.uk Title: Pairwise meta-analysis of aggregate data using metaan in Stata Journal: Stata Journal Pages: 680-705 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: A few years ago, we developed metaan, a package to perform fixed- or random-effects meta-analysis. In terms of random-effects meta-analysis, it of- fered a wide choice of models, including maximum likelihood, profile likelihood, or restricted maximum-likelihood, in addition to the established DerSimonian–Laird method available in metan or Cochrane’s RevMan software. Other unique features included a wide range of reported heterogeneity measures and a plot of the max- imum likelihood function. Since then, metaan has been continuously updated to offer improved graphics, more options, and more meta-analysis models. In this necessary update, we describe these additions and discuss the new models and the evidence behind them. Keywords: metaan, meta-analysis, random effects, fixed effect, effect sizes, DerSimonian–Laird, maximum likelihood, profile likelihood, restricted max- imum likelihood, Peto, Mantel–Haenszel, permutation(s) method, bootstrap DerSimonian–Laird, proportions, forest plot File-URL: http://www.stata-journal.com/article.html?article=st0201_1 File-Function: link to article purchase X-DOI: 10.1177/1536867X20953575 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0201_1/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:680-705 Template-Type: ReDIF-Article 1.0 Author-Name: Jia Li Author-Email: jl410@duke.edu Author-WorkPlace-Name: Duke University Author-Name: Zhipeng Liao Author-Email: zhipeng.liao@ucla.edu Author-WorkPlace-Name: University of California–Los Angeles Author-Name: Mengsi Gao Author-Email: mengsi.gao@berkeley.edu Author-WorkPlace-Name: University of California–Berkeley Title: Uniform nonparametric inference for time series using Stata Journal: Stata Journal Pages: 706-720 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: In this article, we introduce a command, tssreg, that conducts non-parametric series estimation and uniform inference for time-series data, including the case with independent data as a special case. This command can be used to nonparametrically estimate the conditional expectation function and the uniform confidence band at a user-specified confidence level, based on an econometric the- ory that accommodates general time-series dependence. The uniform inference tool can also be used to perform nonparametric specification tests for conditional moment restrictions commonly seen in dynamic equilibrium models. Keywords: tssreg, nonparametric regression, Newey–West standard error, series estimation, specification test, uniform inference File-URL: http://www.stata-journal.com/article.html?article=st0614 File-Function: link to article purchase X-DOI: 0.1177/1536867X20953576 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0614/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:706-720 Template-Type: ReDIF-Article 1.0 Author-Name: Jeronimo Oliveira Muniz Author-Workplace-Name: Universidade Federal de Minas Gerais Author-Email: jeronimo@ufmg.br Title: Multistate life tables using Stata Journal: Stata Journal Pages: 721-745 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: The mslt command calculates the functions of a multistate life table and plots a graph of conditional and unconditional life expectancies by time. The command provides linear and exponential solutions to estimate the number of individuals, transitions, probabilities, person-years, and years of life in a given cohort and state of occupancy. The input data are time-specific transition rates (or survivorship proportions) between nonabsorbing and at most one absorbing state. In addition to the mean age at transfer between states, mslt calculates the following summary measures: the mean age, the probability of dying, the average duration, and the proportion of life spent in a specific state. Keywords: mslt, age, demography, increment–decrement, life expectancy, life table, model, multigroup, multistate, population, probability, proportion, rate, transition File-URL: http://www.stata-journal.com/article.html?article=st0615 File-Function: link to article purchase X-DOI: 10.1177/1536867X2095357 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0615/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:721-745 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: Is a variable constant? Journal: Stata Journal Pages: 746-756 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: Whether a variable is in fact constant—so that it takes on exactly the same value in different observations—is of common concern in statistical work. The question may arise for all the observations in a dataset or for different subsets of the dataset. It may arise because constancy is desirable or because constancy is undesirable, but either way we often need to check simply and quickly. In this column, I discuss several methods for checking. I do not claim to offer a complete analysis of why you might or should care but will give examples arising from experience. Keywords: data management, panel data, longitudinal data, missing values, summarize, assert, tabulate, indicator variables File-URL: http://www.stata-journal.com/article.html?article=dm0103 File-Function: link to article purchase X-DOI: 10.1177/1536867X19874247 Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/dm0103/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:746-756 Template-Type: ReDIF-Article 1.0 Author-Name: Editors Author-Email: editors@stata.com Title: Software updates Journal: Stata Journal Pages: 757-758 Issue: 3 Volume: 20 Year: 2020 Month: September Abstract: Updates for previously published packages are provided. Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/gr0066_2/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0375_3/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0383_1/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0449_1/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0524_2/ Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj20-3/st0533_1/ Handle:RePEc:tsj:stataj:v:20:y:2020:i:3:p:757-758