Pub Date : 2023-12-01DOI: 10.1177/1536867X231212446
Sebastian Kripfganz
In this article, I review Microeconometrics Using Stata, Second Edition, by A. Colin Cameron and Pravin K. Trivedi (2022, Stata Press).
在本文中，我将对 A. Colin Cameron 和 Pravin K. Trivedi 所著的《使用 Stata 的微观计量经济学》第二版（2022 年，Stata 出版社）进行评述。
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Pub Date : 2023-12-01DOI: 10.1177/1536867x231212453
Nicholas J. Cox
Percentage breakdowns for a series of classes or categories are sometimes reported without a specification of class frequencies or even the total sample size. This column surveys the problem of estimating the minimum sample size and class frequencies consistent with a reported breakdown and a particular resolution. I introduce and explain a new command, find_denom. Rounding quirks whereby a total is reported as above or below 100% are discussed as a complication.
{"title":"Speaking Stata: Finding the denominator: Minimum sample size from percentages","authors":"Nicholas J. Cox","doi":"10.1177/1536867x231212453","DOIUrl":"https://doi.org/10.1177/1536867x231212453","url":null,"abstract":"Percentage breakdowns for a series of classes or categories are sometimes reported without a specification of class frequencies or even the total sample size. This column surveys the problem of estimating the minimum sample size and class frequencies consistent with a reported breakdown and a particular resolution. I introduce and explain a new command, find_denom. Rounding quirks whereby a total is reported as above or below 100% are discussed as a complication.","PeriodicalId":437120,"journal":{"name":"The Stata Journal","volume":"111 ","pages":"1086 - 1095"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138988203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1177/1536867X231212445
J. Terza
“Simpler standard errors for two-stage optimization estimators” (Terza, 2016a, Stata Journal 16: 368–385) offers an analytic simplification of the daunting textbook formulations of the asymptotic variance–covariance matrix of a class of two-stage optimization estimators. Here I revisit that simplification and show that it applies to a much broader class of estimators than was originally considered. I also offer a correction that further enhances the generality of this asymptotic variance–covariance matrix formulation. These points are illustrated via a real-data application.
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Pub Date : 2023-12-01DOI: 10.1177/1536867X231212448
Andrew Musau
In this article, I review A Gentle Introduction to Stata, Revised Sixth Edition, by Alan Acock (2023, Stata Press).
在本文中，我将评述 Alan Acock 所著的《Stata 入门》（修订第六版）（2023 年，Stata 出版社）。
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Pub Date : 2023-12-01DOI: 10.1177/1536867X231212425
Nicholas J. Cox, Stephen P. Jenkins
{"title":"The Stata Journal Editors’ Prize 2023: Fernando Rios-Avila","authors":"Nicholas J. Cox, Stephen P. Jenkins","doi":"10.1177/1536867X231212425","DOIUrl":"https://doi.org/10.1177/1536867X231212425","url":null,"abstract":"","PeriodicalId":437120,"journal":{"name":"The Stata Journal","volume":"216 1","pages":"905 - 908"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139013568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Introduction to Stata for Health Researchers review","authors":"Tim Collier","doi":"10.1177/1536867X231212452","DOIUrl":"https://doi.org/10.1177/1536867X231212452","url":null,"abstract":"In this article, I review An Introduction to Stata for Health Researchers, by Svend Juul and Morten Frydenberg (2021, Stata Press).","PeriodicalId":437120,"journal":{"name":"The Stata Journal","volume":"332 4","pages":"1082 - 1085"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139022871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.1177/1536867X231212439
Daniele Spinelli
Matrix manipulation in Stata can be a time-consuming and tedious task, especially when it is necessary to subset or rearrange elements from large matrices based on nonconsecutive elements. Compared with Mata, these tasks require more time, more code, and sometimes more complex output. The purpose of this article is to introduce submatrix, a command to manipulate matrix elements using row (and column) names, numbers, and equations.
在 Stata 中进行矩阵操作可能是一项耗时而乏味的任务，尤其是需要根据非连续元素对大型矩阵中的元素进行子集或重新排列时。与 Mata 相比，这些任务需要更多时间、更多代码，有时还需要更复杂的输出。本文旨在介绍 submatrix，这是一条使用行（和列）名、数字和等式操作矩阵元素的命令。
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Pub Date : 2023-12-01DOI: 10.1177/1536867X231212437
Farahnaz Islam, James F. Thrasher, Feifei Xiao, Robert R. Moran, James W. Hardin
In this article, we present software that is suitable for use with Stata’s choice modeling suite of commands, which begin with cm. Within the context of choice models, we focus on best–worst data. In such data, respondents are presented a set of choices and are required to select a best and a worst choice from among the alternatives. Optionally, respondents may indicate an opt-out choice, in which no best or worst choice exists in the choice set. Such data are simplified versions of experiments in which respondents rank all the choices. Once best–worst data are collected, there are specific types of data expansions that analysts use to take advantage of both explicit and implicit information. The commands described in this article support data expansion and model estimation.
在本文中，我们将介绍适合与 Stata 的选择建模命令套件（以 cm 开头）一起使用的软件。在选择模型的范围内，我们主要关注最佳-最差数据。在此类数据中，受访者会看到一组选择，并需要从备选方案中选出一个最佳和一个最差的选择。受访者也可以选择 "退出"，即在选择集中不存在最佳或最差选择。这些数据是受访者对所有选择进行排序的实验的简化版。一旦收集到最佳-最差数据，分析师就可以使用特定类型的数据扩展来利用显性和隐性信息。本文介绍的命令支持数据扩展和模型估计。
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