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Cluster randomized controlled trial analysis at the cluster level: The clan command. Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2023-09-22 Jennifer A Thompson,Baptiste Leurent,Stephen Nash,Lawrence H Moulton,Richard J Hayes
In this article, we introduce a new command, clan, that conducts a cluster-level analysis of cluster randomized trials. The command simplifies adjusting for individual- and cluster-level covariates and can also account for a stratified design. It can be used to analyze a continuous, binary, or rate outcome.
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robustpf: A command for robust estimation of production functions Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2023-04-05 Yingyao Hu, Guofang Huang, Yuya Sasaki
We introduce a new command, robustpf, to estimate parameters of Cobb–Douglas production functions. The command is robust against two potential problems. First, it is robust against optimization err...
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artbin: Extended sample size for randomized trials with binary outcomes Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2023-04-05 Ella Marley-Zagar, Ian R. White, Patrick Royston, Friederike M.-S. Barthel, Mahesh K. B. Parmar, Abdel G. Babiker
We describe the command artbin, which offers various new facilities for the calculation of sample size for binary outcome variables that are not otherwise available in Stata. While artbin has been ...
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Finite mixture models for linked survey and administrative data: Estimation and postestimation Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2023-04-05 Stephen P. Jenkins, Fernando Rios-Avila
Researchers use finite mixture models to analyze linked survey and administrative data on labor earnings, while also accounting for various types of measurement error in each data source. Different...
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artcat: Sample-size calculation for an ordered categorical outcome Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2023-04-05 Ian R. White, Ella Marley-Zagar, Tim P. Morris, Mahesh K. B. Parmar, Patrick Royston, Abdel G. Babiker
We describe a new command, artcat, that calculates sample size or power for a randomized controlled trial or similar experiment with an ordered categorical outcome, where analysis is by the proport...
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Extended biasplot command to assess bias, precision, and agreement in method comparison studies Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2023-04-05 Patrick Taffé, Mingkai Peng, Vicki Stagg, Tyler Williamson
Recently, a new statistical methodology to assess the bias and precision of a new measurement method, which circumvents the deficiencies of the Bland and Altman (1986, Lancet 327: 307–310) limits o...
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power swgee: GEE-based power calculations in stepped wedge cluster randomized trials Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2023-01-05 John A. Gallis, Xueqi Wang, Paul J. Rathouz, John S. Preisser, Fan Li, Elizabeth L. Turner
Stepped wedge cluster randomized trials (SW-CRTs) are increasingly being used to evaluate interventions in medical, public health, educational, and social science contexts. With the longitudinal an...
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portfolio: A command for conducting portfolio analysis in Stata Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2023-01-05 Hongbing Zhu, Lihua Yang
Portfolio analysis is widely used in empirical asset pricing to explore the cross-sectional relation between two or more variables. In this article, we introduce the methodology of portfolio analys...
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printcase: A command for visualizing single observations Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2023-01-05 Max D. Weinreb, Jenny Trinitapoli
In this article, we introduce the printcase command, which outputs data from a specific observation into an easy-to-read Microsoft Word or PDF document. printcase allows analysts to focus on a sing...
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qpair: A command for analyzing paired Q-sorts in Q-methodology Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2023-01-05 Noori Akhtar-Danesh, Stephen C. Wingreen
In this article, we introduce qpair as a new command written in Stata for the analysis of paired Q-sorts in Q-methodology, which is used for studying subjective issues and is a combination of quali...
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rcm: A command for the regression control method Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2023-01-05 Guanpeng Yan, Qiang Chen
The regression control method, also known as the panel-data approach for program evaluation (Hsiao, Ching, and Wan, 2012, Journal of Applied Econometrics 27: 705–740; Hsiao and Zhou, 2019, Journal ...
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Conditional evaluation of predictive models: The cspa command Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2023-01-05 Jia Li, Zhipeng Liao, Rogier Quaedvlieg, Wenyu Zhou
In this article, we introduce a new command, cspa, that implements the conditional superior predictive ability test developed in Li, Liao, and Quaedvlieg (2022, Review of Economic Studies 89: 843–8...
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Computing decomposable multigroup indices of segregation Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-10-06 Daniel Guinea-Martin, Ricardo Mora
Eight multigroup segregation indices are decomposable into a between and a within term. They are two versions of 1) the mutual information index, 2) the symmetric Atkinson index, 3) the relative di...
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Panel unit-root tests with structural breaks Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-10-06 Pengyu Chen, Yiannis Karavias, Elias Tzavalis
In this article, we introduce a new community-contributed command called xtbunitroot, which implements the panel-data unit-root tests developed by Karavias and Tzavalis (2014, Computational Statist...
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xtusreg: Software for dynamic panel regression under irregular time spacing Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-10-06 Yuya Sasaki, Yi Xin
We introduce a new command, xtusreg, that estimates parameters of fixed-effects dynamic panel regression models under unequal time spacing. After reviewing the method, we examine the finite-sample ...
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ivcrc: An instrumental-variables estimator for the correlated random-coefficients model Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-10-06 David Benson, Matthew A. Masten, Alexander Torgovitsky
We discuss the ivcrc command, which implements an instrumental-variables (IV) estimator for the linear correlated random-coefficients model. The correlated random-coefficients model is a natural ge...
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A Stata implementation of second-generation p-values Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-10-06 Sven-Kristjan Bormann
In this article, I introduce new commands to calculate second-generation p-values (SGPVs) for common estimation commands in Stata. The sgpv command and its companions allow the easy calculation of ...
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Bunching estimation of elasticities using Stata Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-10-06 Marinho Bertanha, Andrew H. McCallum, Alexis Payne, Nathan Seegert
Typical censoring models have mass points at the upper or lower tails, or at both tails, of an otherwise continuous outcome distribution. In contrast, we consider a censoring model with a mass poin...
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Flexible parametric survival analysis with multiple timescales: Estimation and implementation using stmt Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-10-06 Hannah Bower, Therese M.-L. Andersson, Michael J. Crowther, Paul C. Lambert
In this article, we describe methodology that allows for multiple timescales using flexible parametric survival models without the need for time splitting. When one fits flexible parametric surviva...
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The inverse hyperbolic sine transformation and retransformed marginal effects Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-10-06 Edward C. Norton
In this article, I show how to calculate consistent marginal effects on the original scale of the outcome variable in Stata after estimating a linear regression with a dependent variable that has b...
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uirt: A command for unidimensional IRT modeling Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-06-30 Bartosz Kondratek
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 extended item-fit analysis capabilities, features multigroup modeling, allows testing for differential
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Average treatment effect estimates robust to the “limited overlap” problem: robustate Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-06-30 Yuya Sasaki, Takuya Ura
We introduce a new command, robustate, that executes the inverseprobability 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 command is demonstrated
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Testing for time-varying Granger causality Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-06-30 Christopher F. Baum, Stan Hurn, Jesús Otero
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.
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Smoothed instrumental variables quantile regression Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-06-30 David M. Kaplan
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 command offers several advantages over the existing ivqreg and ivqreg2 commands for estimating this instrumental variables quantile regression model, which complements the alternative
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Stata tip 146: Using margins after a Poisson regression model to estimate the number of events prevented by an intervention Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-06-30 Milena Falcaro, Roger B. Newson, Peter Sasieni
After fitting a Poisson regression model to evaluate the effect of an intervention in a cohort study, one might be interested in estimating the number of events prevented by the intervention (assuming the observed associations are causal). This can be derived as the difference in the intervention group between the predicted number of events under the counterfactual (no intervention) and the factual
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kpsstest: A command that implements the Kwiatkowski, Phillips, Schmidt, and Shin test with sample-specific critical values and reports p-values Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-06-30 Ali Kagalwala
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 contains a unit root. Such tests have low power again...
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Fitting spatial autoregressive logit and probit models using Stata: The spatbinary command Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-06-30 Daniele Spinelli
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 spregres...
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Testing axioms of revealed preference in Stata Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-06-30 Marcos Demetry, Per Hjertstrand, Matthew Polisson
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 ...
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Estimating the complier average causal effect via a latent class approach using gsem Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-06-30 Patricio Troncoso, Ana Morales-Gómez
In randomized controlled trials, intention-to-treat analysis is customarily used to estimate the effect of the trial. However, in the presence of noncompliance, this can often lead to biased estima...
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Interactively building table reports with basetable Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-06-30 Niels Henrik Bruun
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...
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Binned scatterplots with marginal histograms: binscatterhist Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-06-30 Matteo Pinna
I introduce binscatterhist, a command that extends the functionality of the popular binscatter command (Stepner, 2013, Statistical Software Components S457709, Department of Economics, Boston Colle...
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Speaking Stata: The largest five—A tale of tail values Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-06-30 Nicholas J. Cox
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...
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Announcement of the Stata Journal Editors’ Prize 2022 Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05 Nicholas J. Cox, Stephen P. Jenkins
The editors of the Stata Journal are pleased to invite nominations for their 2022 prize in accordance with the following rules. Nominations should be sent as private email to editors@stata-journal.com by July 31, 2022.
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Simulating time-to-event data from parametric distributions, custom distributions, competing-risks models, and general multistate models Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05 Michael J. Crowther
In this article, I describe some substantial extensions to the survsim command for simulating survival data. survsim can now simulate survival data from a parametric distribution, a custom or user-defined distribution, a fitted merlin model, a specified cause-specific hazards competing-risks model, or a specified general multistate model (with multiple timescales). Left-truncation (delayed entry) is
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A toolbox for measuring heterogeneity and efficiency using zonotopes Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05 Marco Cococcioni, Marco Grazzi, Le Li, Federico Ponchio
In this work, we describe the new command zonotope, which, by resorting to a geometry-based approach, provides a measure of productivity that fully accounts for the existing heterogeneity across firms within the same industry. The method we propose also enables assessment of the extent of multidimensional heterogeneity with applications to fields beyond that of production analysis. Finally, we detail
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Interpreting logit models Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05 Luca J. Uberti
The parameters of logit models are typically difficult to interpret, and the applied literature is replete with interpretive and computational mistakes. In this article, I review a menu of options to interpret the results of logistic regressions correctly and effectively using Stata. I consider marginal effects, partial effects, (contrasts of) predictive margins, elasticities, and odds and risk ratios
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Computing the fragility index for randomized trials and meta-analyses using Stata Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05 Ariel Linden
In this article, I introduce two commands for computing the fragility index (FI): fragility, which is used for individual randomized controlled trials, and metafrag, which is used for meta-analyses. The FI for individual studies is defined as the minimum number of patients whose status would have to change from a nonevent to an event to nullify a statistically significant result. Correspondingly, the
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Travel distance and travel time using Stata: New features and major improvements in georoute Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05 Sylvain Weber, Martin Péclat, August Warren
The community-contributed command georoute is designed to calculate travel distance and travel time between two addresses or two geographical points identified by their coordinates. Since its conception and description by Weber and Péclat (2017, Stata Journal 17: 962–971), the command has been gradually maintained and enriched. The new version of georoute presented in this article encompasses major
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Measuring technical efficiency and total factor productivity change with undesirable outputs in Stata Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05 Daoping Wang, Kerui Du, Ning Zhang
In this article, we introduce two community-contributed data envelopment analysis commands for measuring technical efficiency and productivity change in Stata. Over the last decades, an important theoretical progression of data envelopment analysis, a nonparametric method widely used to assess the performance of decision-making units, is the incorporation of undesirable outputs. Models able to deal
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Binary contrasts for unordered polytomous regressors Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05 Jeremy Freese, Sasha Johfre
In observational studies, regression coefficients for categorical regressors are overwhelmingly presented in terms of contrasts with a reference category. For unordered regressors with many categories, however, this approach often focuses on contrasting different pairs of categories to one another with little substantive rationale for foregrounding some comparisons with others. Mean contrasts, which
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Effect sizes for contrasts of estimated marginal effects Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05 Brian P. Shaw
The statistical literature is replete with calls to report standardized measures of effect size alongside traditional p-values and null hypothesis tests. While effect-size measures such as Cohen’s d and Hedges’s g are straightforward to calculate for t tests, this is not the case for parameters in more complex linear models, where traditional effect-size measures such as η 2 and ω 2 face limitations
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Analyzing coarsened categorical data with or without probabilistic information Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05 Werner Vach, Cornelia Alder, Sandra Pichler
In some applications, only a coarsened version of a categorical outcome variable can be observed. Parametric inference based on the maximum likelihood approach is feasible in principle, but it cannot be covered computationally by standard software tools. In this article, we present two commands facilitating maximum likelihood estimation in this situation for a wide range of parametric models for categorical
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Fitting mixture models for feeling and uncertainty for rating data analysis Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05 Giovanni Cerulli, Rosaria Simone, Francesca Di Iorio, Domenico Piccolo, Christopher F. Baum
In this article, we present the command cub, which fits ordinal rating data using combination of uniform and binomial (CUB) models, a class of finite mixture distributions accounting for both feeling and uncertainty of the response process. CUB identifies the components that define the mixture in the baseline model specification. We apply maximum likelihood methods to estimate feeling and uncertainty
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Stata tip 145: Numbering weeks within months Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05 Nicholas J. Cox
A user wanted a variable to indicate in which week of its month a daily date fell. That question is a challenge both to imagine different definitions of weeks within months and to produce Stata code for each interpretation. It serves as a reminder that whatever date and time problems have been solved, there are still plenty more. See, for example, Cox (2010, 2012a,b, 2018b, 2019) for some previous
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Software Updates Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05
st0376_3: Estimating and modeling relative survival. P. W. Dickman and E. Coviello. Stata Journal 18: 758–759; 17: 515–516; 15: 186–215.
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Announcements Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-04-05
Date: Thursday, May 19, and Friday, May 20, 2022
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Erratum: Unit-root tests for explosive behavior Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-03-01 Christopher F. Baum,Jesús Otero
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Erratum: A comprehensive set of postestimation measures to enrich interrupted time-series analysis Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-03-01 Ariel Linden
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Editorial roles: Farewell and welcome Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-01-06 Nicholas J. Cox
With this issue, a long era in the history of the Stata Journal comes to a close. Editor H. Joseph Newton is retiring at the end of 2021.
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The Stata Journal Editors’ Prize 2021: Mark E. Schaffer Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-01-06 H. Joseph Newton, Nicholas J. Cox
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Software Updates Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-01-06
st0532_3: Event study estimations using Stata: The estudy command. F. Pacicco, L. Vena, and A. Venegoni. Stata Journal 21: 141–151; 19: 497; 18: 461–476.
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Announcements Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-01-06
Date: Thursday, February 10, 2022
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Stata tip 144: Adding variable text to graphs that use a by() option Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-01-06 Nicholas J. Cox
Graphs may be enhanced using a variety of title options, as explained in the help for title options. When the g raph command also uses a by() option naming a variable to categorize data, the subtitle() automatically shows the value on that variable (or the associated value label if defined) of the particular group of observations being shown in each panel.
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Stata tip 142: joinby is the real merge m:m Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-01-06 Deni Mazrekaj, Jesse Wursten
The merge command is one of Stata’s most used commands and works fine as long as the match key is unique in one of the datasets (that is, merge 1:1, 1:m, or m:1 situations). However, when the match key contains duplicates in either dataset, Stata gives an error message saying that the key variable(s) do not uniquely identify observations in master or using dataset. An example can clarify. In jobs.dta
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Implementing the panel event study Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-01-06 Damian Clarke, Kathya Tapia-Schythe
Many studies estimate the impact of exposure to some quasiexperimental policy or event using a panel event study design. These models, as a generalized extension of “difference-in-differences” designs or two-way fixed-effects models, allow for dynamic leads and lags to the event of interest to be estimated, while also controlling for fixed factors (often) by area and time. In this article, we discuss
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Relative distribution analysis in Stata Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-01-06 Ben Jann
In this article, I discuss the method of relative distribution analysis and present Stata software implementing various elements of the methodology. The relative distribution is the distribution of the relative ranks that the outcomes from one distribution take on in another distribution. The methodology can be used, for example, to compare the distribution of wages between men and women. The presented
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Implementing quantile selection models in Stata Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-01-06 Ercio Muñoz, Mariel Siravegna
In this article, we describe qregsel, a community-contributed command that implements a copula-based sample-selection correction for quantile regression recently proposed by Arellano and Bonhomme (2017, Econometrica 85: 1–28). The command allows the user to model selection in quantile regressions by using either a Gaussian or a one-dimensional Frank copula. We illustrate the use of qregsel with two
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On identification and estimation of Heckman models Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-01-06 Jonathan Cook, Joon-Suk Lee, Noah Newberger
In this article, we present commands to enable fixing the value of the correlation between the unobservables in Heckman models. These commands can solve two practical issues. First, for situations in which a valid exclusion restriction is not available, these commands enable exploring how the results could be affected by sample-selection bias. Second, stepping through values of this correlation can
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Unit-root tests for explosive behavior Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-01-06 Christopher F. Baum, Jesús Otero
We present a new command, radf, that tests for explosive behavior in time series. The command computes the right-tail augmented Dickey and Fuller (1979, Journal of the American Statistical Association 74: 427–431) unitroot test and its further developments based on supremum statistics derived from augmented Dickey–Fuller-type regressions estimated using recursive windows (Phillips, Wu, and Yu, 2011
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Meeting assumptions in the estimation of reliability Stata J. Promot. Commun. Stat. Stata (IF 4.8) Pub Date : 2022-01-06 Brian P. Shaw
Researchers and psychometricians have long used Cronbach’s α as a measure of reliability. However, there have been growing calls to replace Cronbach’s α with measures that have more defensible assumptions. One of the most common and straightforward recommended reliability estimates is ω. After a review of reliability and its estimation in Stata, I introduce the community-contributed command omegacoef