第四卷.Stata最新且急需的程序系列汇编

凡是搞计量经济的，都关注这个号了

稿件：econometrics666@126.com

关于Stata相关技能，各位学者可以参阅如下文章：1.Stata16新增功能有哪些? 满满干货拿走不谢，2.Stata资料全分享，快点收藏学习，3.Stata统计功能、数据作图、学习资源等，4.Stata学习的书籍和材料大放送, 以火力全开的势头，5.史上最全Stata绘图技巧, 女生的最爱，6.把Stata结果输出到word, excel的干货方案，7.编程语言中的函数什么鬼？Stata所有函数在此集结，8.世界范围内使用最多的500个Stata程序，9.6张图掌握Stata软件的方方面面, 还有谁, 还有谁?，10.LR检验、Wald检验、LM检验什么鬼？怎么在Stata实现，11.Stata15版新功能，你竟然没有想到，一睹为快，12."高级计量经济学及Stata应用"和"Stata十八讲"配套数据，13.数据管理的Stata程序功夫秘籍，14.非线性面板模型中内生性解决方案以及Stata命令，15.把动态面板命令讲清楚了，对Stata的ado详尽解释，16.半参数估计思想和Stata操作示例，17.Stata最有用的points都在这里,无可替代的材料，18.PSM倾向匹配Stata操作详细步骤和代码，干货十足，19.随机前沿分析和包络数据分析 SFA,DEA 及Stata操作，20.福利大放送, Stata编程技巧和使用Tips大集成，21.使用Stata进行随机前沿分析的经典操作指南，22.Stata, 不可能后悔的10篇文章, 编程code和注解，23.用Stata学习Econometrics的小tips, 第二发礼炮，24.用Stata学习Econometrics的小tips, 第一发礼炮，25.广义合成控制法gsynth, Stata运行程序release，26.多重中介效应的估计与检验, Stata MP15可下载，27.输出变量的描述性统计的方案，28.2SLS第一阶段输出, 截面或面板数据及统计值都行，29.盈余管理指标的构建及其Stata实现程序, 对应解读和经典文献，30.Python, Stata, R软件史上最全快捷键合辑！，31.用Stata做面板数据分析, 操作代码应有尽有，32.用Stata做面板数据分析, 操作代码应有尽有，33.第三卷.Stata最新且急需的程序系列汇编。还有很多相关文章，各位学者可以自行搜索参阅。

必看: 计量经济圈公众号搜索功能及操作流程演示 (戳前面)

Abstract: Lewbel (2012) provides a heteroskedasticity-based estimator for linear regression models containing an endogenous regressor when no external instruments or other such information is available. The estimator is implemented in the command ivreg2h by Baum and Schaffer (2012, Statistical Software Components S457555, Department of Economics, Boston College). In this article, we give advice and instructions to researchers who want to use this estimator.

摘要：当没有外部工具或其他此类信息可用时，Lewbel（2012，《商业与经济统计杂志》 30：67–80）为包含内生回归变量的线性回归模型提供了基于异方差性的估计器。估计器由Baum和Schaffer 在指令ivreg2h中实现（2012年，波士顿学院经济系，统计软件组件S457555）。在本文中，我们向想要使用此估计量的研究人员提供建议和指导。

Abstract: Many applications involve a censored dependent variable, an endogenous independent variable, or both. Chernozhukov, Fernández-Val, and Kowalski (2015, Journal of Econometrics 186: 201–221) introduced a censored quantile instrumental-variable (CQIV) estimator for use in those applications. The estimator has been applied by Kowalski (2016, Journal of Business & Economic Statistics 34: 107–117), among others. In this article, we introduce a command, cqiv, that simplifies application of the CQIV estimator in Stata. We summarize the CQIV estimator and algorithm, describe the use of cqiv, and provide empirical

摘要：许多模型涉及审查的因变量，内生自变量或两者。Chernozhukov，Fernández-Val和Kowalski（2015，Journal of Econometrics 186：201–221）引入了用于这些应用的受限分位数工具变量（CQIV）估计器。该估计量已由Kowalski（2016，商业与经济统计杂志 34：107-117）应用。在本文中，我们介绍了一个命令cqiv，该命令简化了Stata中CQIV估计器的应用。我们总结了CQIV估计器和算法，描述了cqiv的用法，并提供了经验示例。

Abstract: Royston et al.’s (2011, Trials 12: 81) multiarm, multistage (MAMS) framework for the design of randomized clinical trials uses intermediate outcomes to drop research arms early for lack of benefit at interim stages, increasing efficiency in multiarm designs. However, additionally permitting interim evaluation of efficacy on the primary outcome measure could increase adoption of the design and result in practical benefits, such as savings in patient numbers and cost, should any efficacious arm be identified early. The nstage command, which aids the design of MAMS trial designs, has been updated to support this methodological extension. Operating characteristics can now be calculated for a design with binding or nonbinding stopping rules for lack of benefit and with efficacy stopping boundaries. An additional option searches for a design that strongly controls the familywise error rate at the desired level. We illustrate how the new features can be used to design a trial with the drop-down menu, using the original comparisons from the MAMS trial STAMPEDE as an example. The new functionality of the command will serve a broader range of trial objectives and increase efficiency of the design and thus increase uptake of the MAMS design in practice.

摘要：Royston等人（2011，Trials 12：81）的multiarm，多阶段（MAMS）框架用于随机临床试验的设计，由于中间阶段缺乏获益，因此采用中间结果来尽早放弃研究结果，从而提高了multiarm设计的效率。但是，如果可以及早发现任何有效的multiarm，则另外允许对主要结局指标进行疗效的中期评估可能会增加设计的采用率，并带来实际的好处，例如节省患者人数和成本。该nstage辅助MAMS试验设计设计的命令已更新，以支持此方法扩展。现在可以计算具有绑定或非绑定停止规则的设计的运行特性，因为这些规则没有好处，并且具有有效的停止边界。另一个选择是搜索一种将家族错误率严格控制在所需水平的设计。我们以MAMS试用STAMPEDE中的原始比较为例，说明如何使用新功能通过下拉菜单设计试验。该命令的新功能将服务于更广泛的试验目标，并提高设计效率，从而在实践中增加对MAMS设计的采用。

Abstract: Permutation tests are useful in stepped-wedge trials to provide robust statistical tests of intervention-effect estimates. However, the permute command does not produce valid tests in this setting because individual observations are not exchangeable. We introduce the swpermute command, which permutes clusters to sequences to maintain exchangeability. The command provides additional functionality for performing analyses of stepped-wedge trials. In particular, we include the withinperiod option, which performs the specified analysis separately in each period of the study with the resulting period-specific intervention-effect estimates combined as a weighted average. We also include functionality to test nonzero null hypotheses to aid in the construction of confidence intervals. Examples of the application of swpermute are given using data from a trial testing the impact of a new tuberculosis diagnostic test on bacterial confirmation of a tuberculosis diagnosis.

摘要：置换测试在逐步楔入试验中很有用，可提供对干预效果估计值的可靠统计测试。但是，由于单个观测值不可交换，因此permute命令在此设置下不会产生有效的测试。我们引入了swpermute命令，该命令将簇置换为序列以保持可交换性。该命令提供了用于执行逐步楔形试验分析的附加功能。特别是，我们包括withinperiod选项，在研究的每个阶段分别进行指定的分析，并将得出的针对特定阶段的干预效果估计值合并为加权平均值。我们还包括测试非零无效假设的功能，以帮助构建置信区间。使用来自试验的数据给出了swpermute的应用示例，该试验测试了新的结核病诊断测试对结核病诊断的细菌确认的影响。

Abstract: In this article, I introduce the medadhere command, which computes medication adherence rates for two commonly used measures in research and practice: the medication possession ratio and the proportion of days covered. medadhere computes adherence rates for a single medication or multiple medications, and its options provide great flexibility to support the specific needs of the user.

摘要：在本文中，我介绍了medadhere命令，该命令可计算研究和实践中两种常用度量的用药率：用药率和所占天数。medadhere可计算单一药物或多种药物的依从率，其选项提供了极大的灵活性来支持用户的特定需求。

Abstract: In this article, I introduce the distcomp command, which assesses whether two distributions differ at each possible value while controlling the probability of any false positive, even in finite samples. I discuss syntax and the underlying methodology (from Goldman and Kaplan [2018, Journal of Econometrics 206: 143–166]). Multiple examples illustrate the dis-tcomp command, including revisiting the experimental data of Gneezy and List (2006, Econometrica 74: 1365–1384) and the regression discontinuity design of Cattaneo, Frandsen, and Titiunik (2015, Journal of Causal Inference 3: 1–24).

摘要：在本文中，我介绍了distcomp命令，该命令评估即使在有限样本中，两个分布在每个可能的值上是否不同，同时控制任何假阳性的可能性。我讨论了语法和底层方法（来自Goldman和Kaplan [2018，Journal of Econometrics 206：143-166]）。多个示例说明了distcomp命令，包括重新研究Gneezy和List（2006，Econometrica 74：1365-1384）的实验数据以及Cattaneo，Frandsen和Titiunik的回归不连续性设计（2015年，因果推论 3：1-24） ）。

Abstract: In recent decades, econometric tools for handling instrumental-variable regressions characterized by many instruments have been developed. We introduce a command, mivreg, that implements consistent estimation and testing in linear instrumental-variables regressions with many (possibly weak) instruments. mivreg covers both homoskedastic and heteroskedastic environments, estimators that are both nonrobust and robust to error nonnormality and projection matrix limit, and parameter tests and specification tests both with and without correction for existence of moments. We also run a small simulation experiment using mivreg and illustrate how mivreg works with real data.

摘要：在最近的几十年中，开发了用于处理以许多工具变量为特征的工具变量回归的计量经济学工具。我们引入了一个命令mivreg，该命令使用许多（可能是较弱的）工具在线性工具变量回归中实现了一致的估计和测试。mivreg涵盖同方差和异方差两种环境，对误差，非正态性和投影矩阵极限不具有鲁棒性和鲁棒性的估计器，以及带有或不带有力矩存在校正的参数测试和规范测试。我们还使用mivreg运行了一个小型模拟实验，并说明了mivreg如何处理真实数据。

Abstract: Data management tasks include manipulating variables, variable labels, and value labels. While Stata has versatile commands and functions to address the first task, managing variable and value labels is not as convenient. In this article, I introduce a new command, elabel, that enhances the capabilities of Stata’s label commands. I discuss these enhancements using various examples. I also demonstrate how to add new commands to elabel.

摘要：数据管理任务包括操作变量，变量标签和值标签。虽然Stata具有通用的命令和功能来解决第一个任务，但是管理变量和值标签却不那么方便。在本文中，我介绍了一个新命令elabel，该命令增强了Stata的lab el命令的功能。我将使用各种示例来讨论这些增强功能。我还将演示如何向elabel添加新命令。

Abstract: In this article, we review Granger causality tests that are robust to the presence of instabilities in a vector autoregressive framework. We also introduce the gcrobustvar command, which illustrates the procedure in Stata. In the presence of instabilities, the Granger causality robust test is more powerful than the traditional Granger causality test.

Abstract: Most Stata commands allow cluster(varname) or vce(cluster clustvar) as an option, popularizing the use of standard errors that are robust to oneway clustering. For adjusting standard errors for multiway clustering, there is no solution that is as widely applicable. While several community-contributed packages support multiway clustering, each package is compatible only with a subset of models that Stata’s ever-expanding library of commands allows the researcher to fit. We introduce a command, vcemway, that provides a one-stop solution for multiway clustering. vcemway works with any estimation command that allows cluster(varname) as an option, and it adjusts standard errors, individual significance statistics, and confidence intervals in output tables for multiway clustering in specified dimensions. The covariance matrix used in making this adjustment is stored in e(V), meaning that any subsequent call to postestimation commands that use e(V) as input (for example, test and margins) will also produce results that are robust to multiway clustering.

摘要：大多数Stata命令都允许使用cluster（varname ）或vce（cluster clustvar ）作为选项，从而普及了对单向集群具有鲁棒性的标准错误。为了调整多向群集的标准错误，没有广泛适用的解决方案。尽管几个社区提供的软件包都支持多路集群，但是每个软件包仅与Stata不断扩展的命令库允许研究人员适应的模型子集兼容。我们引入了vcemway命令，该命令为多路集群提供了一站式解决方案。vcemway可与任何允许群集的估计命令配合使用（varname ）（可选）针对特定维度的多路聚类，它会调整输出表中的标准误差，单个显着性统计数据和置信区间。进行此调整时使用的协方差矩阵存储在e（V）中，这意味着使用e（V）作为输入的对后估计命令的任何后续调用（例如test和margin）也将产生对多路聚类具有鲁棒性的结果。

Abstract: Standard mediation techniques for fitting mediation models cannot readily be translated to nonlinear regression models because of scaling issues. Methods to assess mediation in regression models with categorical and limited response variables have expanded in recent years, and these techniques vary in their approach and versatility. The recently developed khb technique purports to solve the scaling problem and produce valid estimates across a range of nonlinear regression models. Prior studies demonstrate that khb performs well in binary logistic regression models, but performance in other models has yet to be investigated. In this article, we evaluate khb‘s performance in fitting ordinal logistic regression models as an exemplar of the wider set of models to which it applies. We examined performance across 38,400 experimental conditions involving sample size, number of response categories, distribution of variables, and amount of mediation. Results indicate that under all experimental conditions, khb estimates the difference (mediation) coefficient and its associated standard error with little bias and that the nominal confidence interval coverage closely matches the actual. Our results suggest that researchers using khb can assume that the routine reasonably approximates population parameters.

摘要：由于存在缩放问题，用于拟合中介模型的标准中介技术无法轻易转换为非线性回归模型。近年来，在具有分类响应和有限响应变量的回归模型中评估中介的方法已经扩展，这些技术的方法和通用性各不相同。最近开发的khb技术旨在解决缩放问题并在一系列非线性回归模型中产生有效的估计。先前的研究表明k hb在二元logistic回归模型中表现良好，但在其他模型中的性能尚待研究。在本文中，我们评估khb在拟合序数逻辑回归模型中的表现，是其适用的更广泛的模型集的典范。我们在38,400个实验条件下检查了性能，这些条件包括样本量，响应类别数，变量分布和中介量。结果表明，在所有实验条件下，khb都以很小的偏差估计差（中介）系数及其相关的标准误差，并且名义置信区间的覆盖范围与实际值非常接近。我们的结果表明，使用khb的研究人员可以假设该例程合理地近似了种群参数。

Abstract: In this article, I present the community-contributed stmixed command for fitting multilevel survival models. It serves as both an alternative to Stata’s official mestreg command and a complimentary command with substantial extensions. stmixed can fit multilevel survival models with any number of levels and random effects at each level, including flexible spline-based approaches (such as Royston–Parmar and the log-hazard equivalent) and user-defined hazard models. Simple or complex time-dependent effects can be included, as can expected mortality for a relative survival model. Left-truncation (delayed entry) is supported, and t-distributed random effects are provided as an alternative to Gaussian random effects. I illustrate the methods with a commonly used dataset of patients with kidney disease suffering recurrent infections and a simulated example illustrating a simple approach to simulating clustered survival data using survsim (Crowther and Lambert 2012, Stata Journal 12: 674–687; 2013, Statistics in Medicine 32: 4118–4134). stmixed is part of the merlin family (Crowther 2017, arXiv Working Paper No. arXiv:1710.02223; 2018, arXiv Working Paper No. arXiv:1806.01615).

摘要：在这篇文章中，我提出了社区贡献的stmixed拟合多层生存模型命令。它既可以替代Stata的官方mestreg命令，也可以作为带有实质性扩展的免费命令。stmixed可以适合具有任意数量级别和每个级别的随机影响的多级生存模型，包括基于样条的灵活方法（例如Royston–Parmar和对数危害等价物）和用户定义的危害模型。可以包括简单或复杂的时间依赖性效应，也可以包括相对生存模型的预期死亡率。支持左截断（延迟条目），并且t提供了分布随机效应，以替代高斯随机效应。我用常用的患有反复感染的肾脏疾病患者的数据集说明了这些方法，并给出了一个模拟示例，说明了使用survsim模拟聚类生存数据的简单方法（Crowther和Lambert 2012，Stata Journal 12：674-687；2013，Statistics in医学 32：4118-4134）。stmixed是merlin家族的一部分（Crowther 2017，arXiv工作文件编号arXiv：1710.02223; 2018，arXiv工作文件编号arXiv：1806.01615）。

Abstract: When one analyzes the determinants of production efficiency, regressing efficiency scores estimated by data envelopment analysis on explanatory variables has much intuitive appeal. Simar and Wilson (2007, Journal of Econometrics 136: 31–64) show that this conventional two-stage estimation procedure suffers from severe flaws that render its results, and particularly statistical inference based on them, questionable. They additionally propose a statistically grounded bootstrap-based two-stage estimator that eliminates the above-mentioned weaknesses of its conventional predecessors and comes in two variants. In this article, we introduce the new command simarwilson, which implements either variant of the suggested estimator in Stata. The command allows for various options and extends the original procedure in some respects. For instance, it allows for analyzing both outputand input-oriented efficiency. To demonstrate the capabilities of simarwilson, we use data from the Penn World Tables and the Global Competitiveness Report by the World Economic Forum to perform a cross-country empirical study about the importance of quality of governance in a country for its efficiency of output production.

摘要：当分析生产效率的决定因素时，通过数据包络分析对解释变量估计的回归效率得分具有很强的直观吸引力。Simar and Wilson（2007，Journal of Econometrics 136：31–64）表明，这种传统的两阶段估计程序存在严重缺陷，这些缺陷使结果（尤其是基于它们的统计推断）令人怀疑。他们还提出了一种基于统计的基于Bootstrap的两阶段估计器，该估计器消除了其传统前辈的上述缺点，并提供了两种变体。在本文中，我们介绍了新命令simarwilson，它在Stata中实现建议的估算器的任一变体。该命令允许使用各种选项，并在某些方面扩展了原始过程。例如，它可以分析输出效率和面向输入的效率。为了证明simarwilson的能力，我们使用了Penn世界表和世界经济论坛的《全球竞争力报告》中的数据，进行了关于一个国家的治理质量对其产出生产效率的重要性的跨国实证研究。

Abstract: The spaghetti problem arises in graphics when multiple time series or other functional traces show mostly a tangled mess. Devices to improve on graphical defaults include transformed scales (especially logarithmic scales); trying to increase the graph area showing the data (especially by losing the legend whenever possible); different colors sometimes; subdividing data into a few groups; subtraction to focus on residuals or smoothing to reduce noise; selection or sampling of what is shown or emphasized; and stacking series vertically.

摘要：当多个时间序列或其他功能迹线大多显示出混乱的情况时，图形中会出现spaghetti问题。用于改善图形默认值的设备包括转换后的比例尺（尤其是对数比例尺）；尝试增加显示数据的图形区域（尤其是尽可能地丢失图例）；有时有不同的颜色；将数据细分为几组；减去以集中于残差或平滑以减少噪声；选择或抽样显示或强调的内容；并垂直堆叠系列。

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