第一(二)卷.Stata最新且有趣的程序系列汇编

凡是搞计量经济的，都关注这个号了

稿件：[email protected]

所有计量经济圈方法论丛的code程序, 宏微观数据库和各种软件都放在社群里.欢迎到计量经济圈社群交流访问.

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。还有很多相关文章，各位学者可以自行搜索参阅。
计量社群里有Stata 16，各位群友可以自行下载使用。

正文br/>关于下方文字内容，作者：王炜哲，武汉理工大学经济学院，通信邮箱：[email protected]
今天，我们引荐使用2019年Stata Journal上关于Stata的最新应用进展，对Stata软件及实证研究感兴趣的学者都可以参阅以下文献。
文章1：Fast and wild: Bootstrap inference in Stata using boottest
D. Roodman, J. G. MacKinnon, M. Ø. Nielsen, and M. D. Webb
Abstract: The wild bootstrap was originally developed for regression models with heteroskedasticity of unknown form. Over the past 30 years, it has been extended to models estimated by instrumental variables and maximum likelihood and to ones where the error terms are (perhaps multiway) clustered. Like bootstrap methods in general, the wild bootstrap is especially useful when conventional inference methods are unreliable because large-sample assumptions do not hold. For example, there may be few clusters, few treated clusters, or weak instruments. The package boottest can perform a wide variety of wild bootstrap tests, often at remarkable speed. It can also invert these tests to construct confidence sets. As a postestimation command, boottest works after linear estimation commands, including regress, cnsreg, ivregress, ivreg2, areg, and reghdfe, as well as many estimation commands based on maximum likelihood. Although it is designed to perform the wild cluster bootstrap, boottest can also perform the ordinary (nonclustered) version. Wrappers offer classical Wald, score/Lagrange multiplier, and Anderson–Rubin tests, optionally with (multiway) clustering. We review the main ideas of the wild cluster bootstrap, offer tips for use, explain why it is particularly amenable to computational optimization, state the syntax of boottest, artest, scoretest, and waldtest, and present several empirical examples.
文章1：快速而野性:在Stata自举法推断中应用boottest方法
D. Roodman, J. G. MacKinnon, M. Ø. Nielsen, and M. D. Webb
摘要：Wild bootstrap起源并发展于解决回归方程中未知形式的异方差问题。在过去的30年间，它已经被拓展并应用于工具变量法、最大似然法甚至是扰动项（可能是多方向）聚集的模型中。正如普通的自举抽样法，Wild bootstrap对于解决普通的推断方法中由于大样本假设不成立或不可靠的问题具有奇效。例如，如果存在弱集群、弱被处理集群、或者是弱工具变量的问题。Boottest 程序包通常能够以卓越的速度大量运行Wlid bootstrap测试，并通过转置这些测试来构建置信集。不过，需要注意的是，作为一个后验命令，boottest通常在regress, cnsreg, ivregress, ivreg2, areg, reghdfe以及一些基于最大似然估计的回归命令之后使用。虽然被设计于运行野性集群 bootstrap，bootttest依然能够运行传统（非集群）版本的自举法。它提供了经典的怀特检验，分数/拉格朗日乘数以及安德森-罗宾检验，可选（多路）集群等选项。在这篇文章中，我们回顾了野性集群bootstrap的主要思想，提供其使用技巧，解释其尤其适用计算优化问题的原因，并说明了boottest、artest、scoretest以及waldtest的相关语法，最后给出了几个经验示例。
文章2：Seamless interactive language interfacing between R and Stata
E. F. Haghish
Abstract: In this article, I propose a new approach to language interfacing for statistical software by allowing automatic interprocess communication between R and Stata. I advocate interactive language interfacing in statistical software by automatizing data communication. I introduce the rcall package and provide examples of how the R language can be used interactively within Stata or embedded into Stata programs using the proposed approach to interfacing. Moreover, I discuss the pros and cons of object synchronization in language interfacing.
文章2：R语言和Stata之间的无缝交互语言接口
E. F. Haghish
摘要：在这篇文章中，我提供了一个在R语言以及Stata之间允许自动交互的统计软件方法来解决两者间的语言交互问题。我主张通过以自动数据通信的统计软件来实现语言交互。我介绍了rcall程序包并提供了几个利用我所提出的交互方法来实现R语言与Stata交互或者嵌入Stata程序的案例。进一步，我讨论了语言交互中对象同步的利弊。
文章3：On the importance of syntax coloring for teaching statistics
E. F. Haghish
Abstract: In this article, I underscore the importance of syntax coloring in teaching statistics. I also introduce the statax package, which includes JavaScript and LATEX programs for highlighting Stata code in HTML and LATEX documents. Furthermore, I provide examples showing how to implement this package for developing educational materials on the web or for a cla***oom handout.
文章3：论语法高亮显示在统计学教学中的重要性（其实Stata 16版本已经可以高亮显示了）E. F. Haghish
摘要：在这篇文章中，我强调了语法高亮显示在统计学教学中的重要性，我也介绍了Statax程序包，其中包括JavaScript和LATEX程序，用于在HTML和LATEX文档中突出显示Stata格式代码。另外，我还提供了几个例子用于展示如何使用这些程序包来开放网络教学资源和课堂讲义。
文章4：Estimation methods in the presence of corner solutions
A. Sánchez-Peñalver
Abstract: In this article, I introduce a new command, nehurdle, that collects maximum likelihood estimators for linear, exponential, homoskedastic, and heteroskedastic tobit; truncated hurdle; and type II tobit models that involve explained variables with corner solutions. I review what a corner solution is as well as the assumptions of the mentioned models.
文章4：存在角点解的估计方法
A. Sánchez-Peñalver
摘要：在这篇文章中，我推出了一个新命令 nehurdle。该命令可以用于收集线性，指数，同方差，异方差tobit，截断栅栏的最大似然估计值，以及II类型tobit模型的估计值。我回顾了什么是角点解以及估计上述模型的主要方法。
文章5：piaactools: A program for data analysis with PIAAC data
M. Jakubowski and A. Pokropek
Abstract: The OECD Programme for the International Assessment of Adult Competencies (PIAAC) is currently the only international survey of adult skills. It provides rich data on skills, work and life situations, earnings, and attitudes. To ensure representativeness and high reliability, the study is based on a complex survey design and advanced statistical methods. To obtain correct results from publicly available microdata, one must use special methods that are often too advanced for less experienced researchers. In this article, we present piaactools—a package of three commands that facilitate analysis with PIAAC data. The command piaacdes calculates basic statistics,piaactab computes frequencies of adults at each proficiency level, and piaacreg allows for the use of several regression models with PIAAC data. Output is saved as HTML files that can be opened in most spreadsheets and as Stata matrices that can be further processed in Stata. We also explain how to use these commands and provide examples that can be easily modified for use with different models and variables.
文章5：Piaactools：一个对PIAAC数据进行数据分析的程序
M. Jakubowski and A. Pokropek
摘要：OECE所组织的国际成人能力评估计划（PIAAC）是目前仅有的对成人能力评估的国际调查。它提供了大量关于技能、工作、生活状况、收入和态度的相关数据。为了确保研究的代表性以及高可靠性，本研究采取了复杂的调查设计和先进的统计方法。为了确保能从公开微观数据中获得正确的结果，必须使用特别的方法，虽然对于经验不足的人员而言，这些方法未免过于先进。在本文，我们介绍了piaactools—一个包含了三个命令的程序包，其能够便利分析PIAAC中的数据。Piaacdes命令用于计算基本统计分析，piaactab命令用于计算成年人熟练水平的频率，piaacreg则提供使用多种PIAAC数据类型的回归模型。输出被保存为可以在大多数电子表格中打开的HTML格式文件以及可以在Stata中进一步处理的Stata矩阵。最后，我们也解释了如何使用这些命令并提供了几个案例帮助我们了解如何轻易修改不同模型和变量。
文章6：lsemantica: A command for text similarity based on latent semantic analysis
C. Schwarz
Abstract: In this article, I present the lsemantica command, which implements latent semantic analysis in Stata. Latent semantic analysis is a machine learning algorithm for word and text similarity comparison and uses truncated singular value decomposition to derive the hidden semantic relationships between words and texts.lsemantica provides a simple command for latent semantic analysis as well as complementary commands for text similarity comparison.
文章6：Isemantica:一个基于潜在语义分析的文本相似性命令
C. Schwarz
摘要：在这篇文章中，我提出了Isemantica命令，该命令在Stata中实现了潜在语义分析。潜在语义分析是一种用于单词和文本相似性比较的机器学习算法，它使用截断的奇异值分解来得出单词和纯文本之间的隐藏语义关系。Isemantica命令也提供了一个用于潜在语义分析的简单命令以及用于比较文本相似性比较的命令。
文章7：Updates to the ipfraking ecosystem
S. Kolenikov
Abstract: Kolenikov (2014, Stata Journal 14: 22–59) introduced the package ipfraking for iterative proportional fitting (raking) weight-calibration procedures for complex survey designs. In this article, I briefly describe the original package and updates to the core program and document additional programs that are used to support the process of creating survey weights in the author’s production code.
文章7：Ipfraking生态系统的更新
S. Kolenikov
摘要：Kolenikov(2014)介绍了用于复杂测量设计的迭代比例拟合（倾斜）效准的ipfraking程序。在本文中，我简要介绍了原始程序包并更新其核程序。另外，我还记录了在作者生产代码过程中可用于创建调查权重的额外程序。
文章8：Bootstrap pointwise confidence intervals for covariate-adjusted survivor functions in the Cox model
C. Ruhe
Abstract: Survival functions are a common visualization of predictions from the Cox model. However, neither Stata’sstcurve command nor the community contributed scurve tvc command allows one to estimate confidence intervals. In this article, I discuss how bootstrap confidence intervals can be formed for covariate-adjusted survival functions in the Cox model. The new bsurvci command automates this procedure and allows users to visualize the results. bsurvci enables one to estimate uncertainty around survival functions estimated from Cox models with time-varying coefficients, a capability that was not previously available in Stata. Furthermore, it provides Stata users with an additional option for survival estimates from Cox models with proportional hazards by allowing them to choose between bootstrap confidence intervals using bsurvci and asymptotic confidence intervals from an existing community-contributed command, survci. Because asymptotic confidence intervals make distributional assumptions when constructing confidence intervals, the bootstrap procedure proposed in this article provides a nonparametric alternative.
文章8：Cox模型中调整协变量生存函数的自举法逐点置信区间
C. Ruhe
摘要：生存函数是Cox模型预测可视化的一个普通的功能。然而，无论是Stata内置的sstcurve或者是社区贡献的 scurve tvc功能都不允许估计置信区间。在本篇文章中，我将讨论如何在Cox模型中为协变量调整后的生存函数形成自举置信区间。这个新的bsurvci命令能够自动完成此过程并允许用户将结果可视化。Bsurvci使人们能够估计生存函数周围的不确定性从具有时变系数的Cox模型，这是Stata以前没有的功能。此外，它还允许Stata用户使用附加选项进行生存估计具有比例风险的Cox模型，方法是允许他们使用bsurvci进行引导置信区间与现有社区贡献的命令survci的渐近置信区间之间进行选择。由于渐近置信区间在构造置信区间时会做出分布假设，因此本文提出的自举程序提供了一种非参数替代方案。
文章9：Candle charts for financial technical analysis
M. F. Dicle
Abstract: Technical analysis is an important part of financial industry, research, and teaching. The methodology has two parts: i) calculation of the individual tools and ii) visual representations. In this article, I provide a community-contributed command, candlechart, to draw the most common technical analysis charts. My intent is to draw these charts similarly to industry examples. The popular candle price chart is combined with charts for volume, moving-average convergence divergence, relative strength index, and Bollinger bands.
文章9：用于财务分析的蜡烛图
M. F. Dicle
摘要：技术分析是金融业，研究和教学的重要组成部分。该方法包括两个部分：i）各个工具的计算和ii）视觉表示。在本文中，我提供了一个社区贡献的命令Candlechart，以绘制最常见的技术分析图。我的意图是绘制类似于行业示例的这些图表。将流行的蜡烛价格图表与体积，移动平均收敛散度，相对强度指数和布林带的图表结合在一起。
文章10：Power calculations for regression-discontinuity designs
M. D. Cattaneo, R. Titiunik, and G. Vazquez-Bare
Abstract: In this article, we introduce two commands, rdpow and rdsampsi, that conduct power calculations and survey sample selection when using local polynomial estimation and inference methods in regression-discontinuity designs. rdpow conducts power calculations using modern robust bias-corrected local polynomial inference procedures and allows for new hypothetical sample sizes and bandwidth selections, among other features.rdsampsi uses power calculations to compute the minimum sample size required to achieve a desired level of power, given estimated or user-supplied bandwidths, biases, and variances. Together, these commands are useful when devising new experiments or surveys in regression-discontinuity designs, which will later be analyzed using modern local polynomial techniques for estimation, inference, and falsification. Because our commands use the communitycontributed (and R) package rdrobust for the underlying bandwidths, biases, and variances estimation, all the options currently available in rdrobust can also be used for power calculations and sample-size selection, including preintervention covariate adjustment, clustered sampling, and many bandwidth selectors. Finally, we also provide companion R functions with the same syntax and capabilities.
文章10：断点回归设计的功效计算
M. D. Cattaneo, R. Titiunik, and G. Vazquez-Bare
摘要：在本文中，我们介绍了两个命令rdpow和rdsampsi，它们在断点回归设计中使用局部多项式估计和推断方法时进行功效计算和调查样本选择。rdpow使用现代的经过稳健性偏差校正的局部多项式推论程序进行功率计算，并允许新的假设样本大小和带宽选择以及其他功能。rdsampsi在给定估计的或用户提供的带宽，偏差和方差的情况下，使用功率计算来计算达到所需功率水平所需的最小样本量。总之，这些命令在断点回归设计中设计新的实验或调查时非常有用，稍后将使用现代局部多项式技术对其进行分析，以进行估计，推断和伪造。由于我们的命令使用社区贡献（和R）软件包rdrobust进行基础带宽，偏差和方差估计，因此rdrobust中当前可用的所有选项还可用于功效计算和样本大小选择，包括干预前协变量调整，聚类样本和许多带宽选择器。最后，我们还提供了具有相同语法和功能的配套R函数。
文章11：Speaking Stata: How best to generate indicator or dummy variables
N. J. Cox and C. B. Schechter
Abstract: Indicator or dummy variables record whether some condition is true or false in each observation by a value of 1 or 0. Values may also be missing if truth or falsity is not known, and that fact should be flagged. Such indicators may be created on the fly by using factor-variable notation. tabulate also offers one method for automating the generation of indicators. In this column, we discuss in detail how otherwise to best generate such variables directly, with comments here and there on what not to do.
文章11：讲故事的状态：如何最好地生成指标或虚拟变量
N. J. Cox and C. B. Schechter
摘要：指示符或伪变量以1或0的值记录每次观察中某个条件是真还是假。如果不知道真伪，则该值也可能会丢失，并且应该标记该事实。可以使用因子变量表示法动态创建此类指标。表格还提供了一种自动生成指标的方法。在本专栏中，我们将详细讨论如何最好地直接生成此类变量，并在此处和该处的注释中说明了不应当做什么。
文章12：qmodel: A command for fitting parametric quantile models
M. Bottai and N. Orsini
Abstract: In this article, we introduce the qmodel command, which fits parametric models for the conditional quantile function of an outcome variable given covariates. Ordinary quantile regression, implemented in the qregcommand, is a popular, simple type of parametric quantile model. It is widely used but known to yield erratic estimates that often lead to uncertain inferences. Parametric quantile models overcome these limitations and extend modeling of conditional quantile functions beyond ordinary quantile regression. These models are flexible and efficient. qmodel can estimate virtually any possible linear or nonlinear parametric model because it allows the user to specify any combination of qmodel-specific built-in functions, standard mathematical and statistical functions, and substitutable expressions. We illustrate the potential of parametric quantile models and the use of the qmodel command and its postestimation commands through realand simulated-data examples that commonly arise in epidemiological and pharmacological research. In addition, this article may give insight into the close connection that exists between quantile functions and the true mathematical laws that generate data.
文章12：qmodel：用于拟合参数分位数模型的命令
M. Bottai and N. Orsini
摘要：在本文中，我们介绍了qmodel命令，该命令适合给定协变量的结果变量的条件分位数函数的参数模型。在qreg命令中实现的普通分位数回归是一种流行的简单类型的参数分位数模型。它被广泛使用，但已知会产生不稳定的估计，这常常导致不确定的推断结果。参数分位数模型克服了这些限制，并扩展了条件分位数函数的建模范围，超越了普通分位数回归。这些模型灵活高效。qmodel实际上可以估计任何可能的线性或非线性参数模型，因为它允许用户指定qmodel的任意组合特定的内置函数，标准数学和统计函数以及可替换的表达式。我们通过流行病学和药理学研究中经常出现的真实和模拟数据示例来说明参数分位数模型的潜力以及qmodel命令及其后估计命令的用法。此外，本文还可以深入了解分位数函数与生成数据的真实数学定律之间存在的紧密联系。
文章13：mixmcm: A community-contributed command for fitting mixtures of Markov chain models using maximum likelihood and the EM algorithm
L. D. F. Saint-Cyr and L. Piet
Abstract: Markov chain models and finite mixture models have been widely applied in various strands of the academic literature. Several studies analyzing dynamic processes have combined both modeling approaches to account for unobserved heterogeneity within a population. In this article, we describe mixmcm, a community-contributed command that fits the general class of mixed Markov chain models, accounting for the possibility of both entries into and exits from the population. To account for the possibility of incomplete information within the data (that is, unobserved heterogeneity), the model is fit with maximum likelihood using the expectation-maximization algorithm. mixmcm enables users to fit the mixed Markov chain models parametrically or semiparametrically, depending on the specifications chosen for the transition probabilities and the mixing distribution. mixmcm also allows for endogenous identification of the optimal number of homogeneous chains, that is, unobserved types or “components”. We illustrate mixmcm‘s usefulness through three examples analyzing farm dynamics using an unbalanced panel of commercial French farms.
文章13：mixmcm：一个社区贡献的命令,用于使用最大似然和EM算法拟合混合马尔可夫链模型
L. D. F. Saint-Cyr and L. Piet
摘要：马尔可夫链模型和有限混合模型已广泛应用于学术文献的各个方面。一些分析动态过程的研究结合了两种建模方法，以解决总体中未观察到的异质性。在本文中，我们描述mixmcm，这是一个由社区贡献的命令，适合混合Markov链模型的一般类别，同时说明了人口进入和退出的可能性。为了说明数据中信息不完整的可能性（即未观察到的异质性），该模型为最大似然拟合模型配备期望最大化算法。mixmcm允许用户根据过渡概率和混合分布选择的规范，以参量或半参量拟合混合马尔可夫链模型。mixmcm还允许内源性识别均质链的最佳数量，即未观察到的类型或“组分”。我们通过使用不平衡的法国商业农场小组分析农场动态的三个示例来说明mixmcm的有用性。
文章14：xtspj: A command for split-panel jackknife estimation
Y. Sun and G. Dhaene
Abstract: In this article, we present a new command, xtspj, that corrects for incidental parameter bias in panel-data models with fixed effects. The correction removes the first-order bias term of the maximum likelihood estimate using the split-panel jackknife method. Two variants are implemented: the jackknifed maximum-likelihood estimate and the jackknifed log-likelihood function (with corresponding maximizer). The model may be nonlinear or dynamic, and the covariates may be predetermined instead of strictly exogenous. xtspjimplements the split-panel jackknife for fixed-effects versions of linear, probit, logit, Poisson, exponential, gamma, Weibull, and negbin2 regressions. It also accommodates other models if the user specifies the log-likelihood function (and, possibly but not necessarily, the score function and the Hessian). xtspj is fast and memory efficient, and it allows large datasets. The data may be unbalanced. xtspj can also be used to compute uncorrected maximum-likelihood estimates of fixed-effects models for which no other xt (see [XT]xt) command exists.
文章14：xtspj：用于拆分面板折刀估计的命令
Y. Sun and G. Dhaene
摘要：在本文中，我们提供了一个新命令xtspj，该命令可以修正具有固定效果的面板数据模型中的偶然参数偏差。该校正使用分屏折刀法去除了最大似然估计的一阶偏差项。实现了两种变体：截断的最大似然估计和截断的对数似然函数（带有相应的最大化器）。该模型可以是非线性或动态的，并且协变量可以是预定的，而不是严格外生的。xtspj为线性，概率，对数，泊松，指数，伽玛，威布尔和negbin2回归的固定效果版本实现拆分面板折刀。如果用户指定对数似然函数（并且可能但不一定是得分函数和Hessian），它也可以容纳其他模型。xtspj快速且内存高效，并且允许大型数据集。而且数据允许不平衡。xtspj还可以用于计算不存在其他xt（请参阅[XT] xt）命令的固定效果模型的未校正最大似然估计。
文章15：Generalized two-part fractional regression with cmp
J. N. Wulff
Abstract: Researchers who model fractional dependent variables often need to consider whether their data were generated by a two-part process. Two-part models are ideal for modeling two-part processes because they allow us to model the participation and magnitude decisions separately. While community-contributed commands currently facilitate estimation of two-part models, no specialized command exists for fitting two-part models with process dependency. In this article, I describe generalized two-part fractional regression, which allows for dependency between models’ parts. I show how this model can be fit using the community-contributed cmp command (Roodman, 2011, Stata Journal 11: 159–206). I use a data example on the financial leverage of firms to illustrate how cmp can be used to fit generalized two-part fractional regression. Furthermore, I show how to obtain predicted values of the fractional dependent variable and marginal effects that are useful for model interpretation. Finally, I show how to compute model fit statistics and perform the RESET test, which are useful for model evaluation.
文章15：使用cmp的广义两部分式分数回归
J. N. Wulff
摘要：对分数因变量建模的研究人员经常需要考虑其数据是否由两部分过程生成。两部分模型非常适合于对两部分过程进行建模，因为它们使我们能够分别对参与决策和规模决策进行建模。虽然社区提供的命令当前有助于两部分模型的估计，但是不存在用于拟合具有过程依赖性的两部分模型的专门命令。在本文中，我描述了广义的两部分式分数回归，它允许模型各部分之间具有依赖性。我展示了如何使用社区提供的cmp命令来拟合该模型（Roodman，2011年，Stata Journal 11：159-206）。我使用有关公司财务杠杆的数据示例来说明CMP如何可用于拟合广义两部分分数回归。此外，我展示了如何获得分数因变量的预测值和对模型解释有用的边际效应。最后，我展示了如何计算模型拟合统计信息并执行RESET测试，这对于模型评估非常有用。
文章16：Statistical analysis of the item-count technique using Stata
C.-l. Tsai
Abstract: In this article, I review recent developments of the item-count technique (also known as the unmatched-count or list-experiment technique) and introduce a new package, kict, for statistical analysis of the item-count data. This package contains four commands: kict deff performs a diagnostic test to detect the violation of an assumption underlying the item-count technique. kict ls and kict ml perform least-squares estimation and maximum likelihood estimation, respectively. Each encompasses a number of estimators, offering great flexibility for data analysis. kict pfci is a postestimation command for producing confidence intervals with better coverage based on profile likelihood. The development of the item-count technique is still ongoing. I will continue to update the kict package accordingly.
文章16：使用Stata进行项目计数技术的统计分析
C.-l. Tsai
摘要：在本文中，我回顾了项目计数技术（也称为不匹配计数或列表实验技术）的最新发展，并介绍了一个新软件包kict，用于对项目计数数据进行统计分析。该软件包包含四个命令：kict deff执行诊断测试，以检测违反项目计数技术基础的假设的情况。kict ls和kict ml分别执行最小二乘估计和最大似然估计。每个都包含许多估算器，为数据分析提供了极大的灵活性。基奇·普菲是后估计命令，用于基于轮廓似然来产生具有更好覆盖率的置信区间。项目计数技术的开发仍在进行中。我将继续相应地更新kict软件包。
文章17：Fuzzy differences-in-differences with Stata
C. de Chaisemartin, X. D'Haultføeuille, and Y. Guyonvarch
Abstract: Differences-in-differences evaluates the effect of a treatment. In its basic version, a “control group” is untreated at two dates, whereas a “treatment group” becomes fully treated at the second date. However, in many applications of this method, the treatment rate increases more only in the treatment group. In such fuzzy designs, de Chaisemartin and D’Haultfœuille (2018b, Review of Economic Studies 85: 999–1028) propose various estimands that identify local average and quantile treatment effects under different assumptions. They also propose estimands that can be used in applications with a nonbinary treatment, multiple periods, and groups and covariates. In this article, we present the command fuzzydid, which computes the various corresponding estimators. We illustrate the use of the command by revisiting Gentzkow, Shapiro, and Sinkinson (2011, American Economic Review 101: 2980–3018).
文章17：Stata的模糊倍差法
C. de Chaisemartin, X. D'Haultføeuille, and Y. Guyonvarch
摘要：倍差法可以评估治疗的效果。在其基本版本中，“对照组”在两个日期没有得到治疗，而“治疗组”在第二个日期得到了充分治疗。但是，在该方法的许多应用中，仅在治疗组中治疗率才会增加。在这样的模糊设计中，de Chaisemartin和D'Haultfœuille（2018b，经济研究评论 85：999-1028）提出了各种估计数，这些估计数在不同的假设下确定了本地平均数和分位数的处理效果。他们还提出了可用于非二进制处理，多个期间以及组和协变量的应用中的估计值。在本文中，我们介绍了命令Fuzzydid，用于计算各种相应的估算器。我们通过重新审视Gentzkow，Shapiro和Sinkinson（2011年，美国经济评论 101：2980-3018）来说明命令的使用。
文章18：Grade functions
J. L. Gallup
Abstract: Student grade processing using Stata is more reliable than methods like spreadsheets and saves the user timeh, especially when courses are repeated. In this article, I introduce functions that automate some useful grade calculations: the functions curve grades according to combinations of a target grade mean, maximum, standard deviation, and percentile cutoff; convert between numerical grades and letter grades; and convert between 0–100 grades and 0–4 grades (grade point average). The functions can also convert between other grading scales, such as those used in other countries.
文章18：成绩函数
J. L. Gallup
摘要：使用Stata进行学生成绩处理比电子表格等方法更可靠，并且可以节省用户时间，尤其当课程重复时。在本文中，我介绍了一些可以自动执行一些有用的坡度计算的函数：这些函数根据目标坡度平均值，最大值，标准偏差和百分位数截止值的组合来弯曲坡度；在数字等级和字母等级之间转换；并在0-100等级和0-4等级之间转换（平均绩点）。这些功能还可以在其他等级量表之间进行转换，例如在其他国家/地区中使用的那些。
文章19：Tips for calculating and displaying risk-standardized hospital outcomes in Stata
J. Lenzi and S. Pildava
Abstract: A major challenge of outcomes research is measuring hospital performance using readily available administrative data. When the outcome measure is mortality or morbidity, rates are adjusted to account for preexisting conditions that may confound their assessment. However, the concept of “risk-adjusted” outcomes is frequently misunderstood. In this article, we try to clarify things, and we describe Stata tools for appropriately calculating and displaying risk-standardized outcome measures. We offer practical guidance and illustrate the application of these tools to an example based on real data (30-day mortality following acute myocardial infarction in Latvia).
文章19：在Stata中计算和显示风险标准化医院结果的提示
J. Lenzi and S. Pildava
摘要：结果研究的主要挑战是使用现成的管理数据来衡量医院的绩效。当结果指标是死亡率或发病率时，调整费率以考虑可能混淆其评估的既往疾病。但是，“风险调整”结果的概念经常被误解。在本文中，我们试图澄清问题，并描述用于适当计算和显示风险标准化结果度量的Stata工具。我们提供了实用指南，并以实际数据（拉脱维亚急性心肌梗死后30天死亡率）为例，说明了这些工具的应用。
第二卷

文章1：Modeling count data with marginalized zero-inflated distributions
T. H. Cummings and J. W. Hardin
Abstract: In this article, we present new commands for modeling count data using marginalized zero-inflated distributions. While we mainly focus on presenting new commands for estimating count data, we also present examples that illustrate some of these new commands.
文章1：使用边际化零膨胀分布对计数数据建模
T. H. Cummings and J. W. Hardin
摘要：在本文中，我们提供了使用边际化零膨胀分布对计数数据进行建模的新命令。尽管我们主要致力于提供用于估算计数数据的新命令，但我们还提供了一些示例，以说明其中的一些新命令。
文章2：kg_nchs: A command for Korn–Graubard confidence intervals and National Center for Health Statistics' Data Presentation Standards for Proportions
B. W. Ward
Abstract: In August 2017, the National Center for Health Statistics (NCHS), part of the U.S. Federal Statistical System, published new standards for determining the reliability of proportions estimated using their data. These standards require one to take the Korn–Graubard confidence interval (CI), CI widths, sample size, and degrees of freedom to assess reliability of a proportion and determine whether it can be presented. The assessment itself involves determining whether several conditions are met. In this article, I present kg_nchs, a postestimation command that is used following svy: proportion. It allows Stata users to a) calculate the Korn–Graubard CI and associated statistics used in applying the NCHS presentation standards for proportions and b) display a series of three dichotomous flags that show whether the standards are met. I provide empirical examples to show how kg_nchs can be used to easily apply the standards and prevent Stata users from needing to perform manual calculations. While developed for NCHS survey data, this command can also be used with data that stem from any survey with a complex sample design.
文章2：kg_nchs：Korn–Graubard置信区间的命令和国家卫生统计中心的比例数据表示标准
B. W. Ward
摘要：2017年8月，美国联邦统计系统一部分的国家卫生统计中心（NCHS）发布了新标准，用于确定使用其数据估算的比例的可靠性。这些标准要求人们采用Korn-Graubard置信区间（CI），CI宽度，样本大小和自由度来评估比例的可靠性并确定是否可以提出。评估本身包括确定是否满足几个条件。在本文中，我提出的kg_nchs命令，一个用于SVY：比例命令之后的 postestimation命令。它允许Stata用户使用：a）计算适用于比例的NCHS表示标准时使用的Korn-Graubard CI和相关统计信息，以及b）显示一系列三个二分旗，显示是否符合标准。我提供了一些经验示例，以说明如何使用kg_nchs轻松应用标准并防止Stata用户进行手动计算。当为NCHS调查数据开发时，此命令也可以与源自具有复杂样本设计的任何调查的数据一起使用。
文章3：konfound: Command to quantify robustness of causal inferences
R. Xu, K. A. Frank, S. J. Maroulis, and J. M. Rosenberg
Abstract: Statistical methods that quantify the discourse about causal inferences in terms of possible sources of biases are becoming increasingly important to many social-science fields such as public policy, sociology, and education. These methods are also known as “robustness or sensitivity analyses”. A series of recent works (Frank [2000, Sociological Methods and Research 29: 147–194]; Pan and Frank [2003, Journal of Educational and Behavioral Statistics 28: 315– 337]; Frank and Min [2007, Sociological Methodology 37: 349–392]; and Frank et al. [2013, Educational Evaluation and Policy Analysis 35: 437–460]) on robustness analysis extends earlier methods. We implement these recent developments in Stata. In particular, we provide commands to quantify the percent bias necessary to invalidate an inference from a Rubin causal model framework and the robustness of causal inferences in terms of correlations associated with unobserved variables.
文章3：konfound：用于量化因果推断的鲁棒性的命令
R. Xu, K. A. Frank, S. J. Maroulis, and J. M. Rosenberg
摘要：根据偏差的可能来源量化因果推理的论述的统计方法对于许多社会科学领域（例如公共政策，社会学和教育）变得越来越重要。这些方法也称为“稳健性或敏感性分析”。一系列最新著作（Frank [2000，社会学方法和研究 29：147-194]；Pan和Frank [2003，教育与行为统计杂志 28：315-337]；Frank和Min [2007，社会学方法 37：349–392]；以及Frank等[2013，教育评估和政策分析35：437–460]）的鲁棒性分析扩展了早期方法。我们在Stata实现这些最新发展。特别是，我们提供了一些命令，用于量化使来自鲁宾因果模型框架的推理无效所需的百分比偏差以及因果推理的鲁棒性（与与未观察变量相关的相关性）。
文章4：Estimation of pre- and posttreatment average treatment effects with binary time-varying treatment using Stata
G. Cerulli and M. Ventura
Abstract: In this article, we describe tvdiff, a community-contributed command that implements a generalization of the difference-in-differences estimator to the case of binary time-varying treatment with pre- and postintervention periods. tvdiff is flexible and can accommodate many actual situations, enabling the user to specify the number of pre- and postintervention periods and a graphical representation of the estimated coefficients. In addition, tvdiff provides two distinct tests for the necessary condition of the identification of causal effects, namely, two tests for the so-called parallel-trend assumption. tvdiff is intended to simplify applied works on program evaluation and causal inference when longitudinal data are available.
文章4：使用Stata估算二值时变处理前后的平均处理效应
G. Cerulli and M. Ventura
摘要：在本文中，我们描述了tvdiff，这是一个由社区贡献的命令，用于对干预前和干预后的二元时变处理情况实现双重差分估计值的一般化。tvdiff灵活，可以适应许多实际情况，使用户可以指定干预前后的次数以及估算系数的图形表示。此外，tvdiff为确定因果关系的必要条件提供了两种不同的检验，即针对所谓的平行趋势假设的两项检验。tvdiff旨在简化纵向数据可用时在程序评估和因果推理方面的应用工作。
文章5：Visualizing effect modification on contrasts
N. H. Bruun
Abstract: A recurring problem in statistics is estimating and visualizing nonlinear dependency between an effect and an effect modifier. One approach to handle this is polynomial regressions of some order. However, polynomials are known for fitting well only in limited ranges. In this article, I present a simple approach for estimating the effect as a contrast at selected values of the effect modifier. I implement this approach using the flexible restricted cubic splines for the point estimation in a new simple command, emc. I compare the approach with other classical approaches addressing the problem.
文章5：可视化对比效果修正
N. H. Bruun
摘要：统计中经常出现的问题是估计和可视化效果与效果修改器之间的非线性相关性。处理此问题的一种方法是某种程度的多项式回归。但是，多项式仅在有限的范围内拟合良好。在本文中，我提出了一种简单的方法，用于以效果修改器的选定值作为对比来估计效果。我在新的简单命令emc中使用灵活的受限三次样条进行点估计来实现此方法。我将这种方法与其他解决该问题的经典方法进行了比较。
文章6：Two-sample instrumental-variables regression with potentially weak instruments
J. Choi and S. Shen
Abstract: We develop a command, weaktsiv, for two-sample instrumentalvariables regression models with one endogenous regressor and potentially weak instruments. weaktsiv includes the classic two-sample two-stage least-squares estimator whose inference is valid only under the assumption of strong instruments. It also includes statistical tests and confidence sets with correct size and coverage probabilities even when the instruments are weak.
文章6：具有潜在弱工具的两样本工具变量回归
J. Choi and S. Shen
摘要：我们为带有一个内生回归变量和潜在弱函数的两样本工具变量回归模型开发了一个命令，weaktsiv。weaktsiv包括经典的两样本两阶段最小二乘估计器，其推论仅在强工具假设下才有效。它还包括具有正确大小和覆盖率概率的统计检验和置信度集，即使工具较弱也是如此。
文章7：Added-variable plots with confidence intervals
J. L. Gallup
Abstract: An added-variable plot is an effective way to show the correlation between an independent variable and a dependent variable conditional on other independent variables. For multivariate estimation, a simple scatterplot showing x versus y is not adequate to show the partial correlation of x with y, because it ignores the impact of the other covariates. Added-variable plots are especially effective for showing the correlation of a dummy x variable with y because the dummy variable conditional on other covariates becomes a continuous variable, making the relationship easier to visualize.Added-variable plots are also useful for spotting influential outliers in the data that affect the estimated regression parameters. Stata provides added-variable plots after ordinary least-squares regressions with theavplot command. I present a new command, avciplot, that adds a confidence interval and other options to theavplot command.
文章7：具有置信区间的加变量图
J. L. Gallup
摘要：加变量图是显示自变量与以其他自变量为条件的因变量之间的相关性的有效方法。对于多变量估计，一个简单的散点图表示X与y不充分显示的部分相关X与ÿ，因为它忽略了其他协变量的影响。附加变量图对于显示虚拟x变量与y的相关性特别有效，因为以其他协变量为条件的虚拟变量变为连续变量，使关系更易于可视化。加变量图还可用于发现影响估计回归参数的数据中有影响的离群值。在使用avplot命令进行普通最小二乘回归之后，Stata提供了可变变量图。我展示了一个新命令avciplot，它向avplot命令添加了置信区间和其他选项。
文章8：cvauroc: Command to compute cross-validated area under the curve for ROC analysis after predictive modeling for binary outcomes
M. A. Luque-Fernandez, D. Redondo-Sánchez, and C. Maringe
Abstract: Receiver operating characteristic (ROC) analysis is used for comparing predictive models in both model selection and model evaluation. ROC analysis is often applied in clinical medicine and social science to assess the tradeoff between model sensitivity and specificity. After fitting a binary logistic or probit regression model with a set of independent variables, the predictive performance of this set of variables can be assessed by the area under the curve (AUC) from an ROC curve. An important aspect of predictive modeling (regardless of model type) is the ability of a model to generalize to new cases. Evaluating the predictive performance (AUC) of a set of independent variables using all cases from the original analysis sample often results in an overly optimistic estimate of predictive performance. One can use K-fold cross-validation to generate a more realistic estimate of predictive performance in situations with a small number of observations. AUC is estimated iteratively for k samples (the “test” samples) that are independent of the sample used to predict the dependent variable (the “training” sample). cvauroc implements k-fold cross-validation for the AUC for a binary outcome after fitting a logit or probit regression model, averaging the AUCs corresponding to each fold, and bootstrapping the cross-validated AUC to obtain statistical inference and 95% confidence intervals. Furthermore, cvauroc optionally provides the cross-validated fitted probabilities for the dependent variable or outcome, contained in a new variable named fit; the sensitivity and specificity for each of the levels of the predicted outcome, contained in two new variables named _senand _spe; and the plot of the mean cross-validated AUC and k-fold ROC curves.
文章8_cvauroc：在对二进制结果进行预测建模之后，计算曲线下交叉验证面积以进行ROC分析的命令
M. A. Luque-Fernandez, D. Redondo-Sánchez, and C. Maringe
摘要：接收器工作特性（ROC）分析用于在模型选择和模型评估中比较预测模型。ROC分析通常用于临床医学和社会科学中，以评估模型敏感性和特异性之间的权衡。在用一组自变量拟合二进制logistic或Probit回归模型后，可以通过ROC曲线的曲线下面积（AUC）来评估这组变量的预测性能。预测建模的一个重要方面（与模型类型无关）是模型能够将其推广到新案例的能力。使用原始分析样本中的所有情况评估一组自变量的预测性能（AUC）通常会导致对预测性能的评估过于乐观。一个可以用K折叠式交叉验证可在只有少量观察值的情况下生成更实际的预测性能估计。对于k个样本（“测试”样本）进行迭代估计AUC ，这与用于预测因变量的样本（“训练”样本）无关。在拟合logit或概率回归模型后，cvauroc对二进制结果的AUC进行k倍交叉验证，对与每个折叠相对应的AUC求平均值，并自举交叉验证的AUC以获得统计推断和95％置信区间。此外，cvauroc可选地为包含在名为fit的新变量中的因变量或结果提供交叉验证的拟合概率。; 对每个预期结果水平的敏感性和特异性，包含在两个新变量sen和_spe中；以及交叉验证的平均AUC和k倍ROC曲线的图。
文章9：The fayherriot command for estimating small-area indicators
C. Halbmeier, A.-K. Kreutzmann, T. Schmid, and C. Schröder
Abstract: We introduce a command, fayherriot, that implements the Fay– Herriot model (Fay and Herriot, 1979, Journal of the American Statistical Association 74: 269–277), which is a small-area estimation technique (Rao and Molina, 2015, Small Area Estimation), in Stata. The Fay–Herriot model improves the precision of area-level direct estimates using area-level covariates. It belongs to the class of linear mixed models with normally distributed error terms. The fayherriot command encompasses options to a) produce out-of-sample predictions, b) adjust nonpositive random-effects variance estimates, and c) deal with the violation of model assumptions.
文章9：用fayherriot命令估算小面积指标
C. Halbmeier, A.-K. Kreutzmann, T. Schmid, and C. Schröder
摘要：我们引入了一个命令fayherriot，该命令实现了Fay–Herriot模型（Fay和Herriot，1979年，美国统计协会杂志74：269–277），这是一种小区域估计技术（Rao和Molina，2015年，《小》面积估算），位于Stata。Fay-Herriot模型使用区域级协变量提高了区域级直接估计的精度。它属于具有正态分布误差项的线性混合模型。fayherriot命令包含以下选项：a）产生样本外预测，b）调整非正向随机效应方差估计，以及c）处理违反模型假设的情况。
文章10：intcount: A command for fitting count-data models from interval data
S. Pudney
Abstract: In this article, I describe a community-contributed command, intcount, that fits one of several regression models for count data observed in interval form. The models available are Poisson, negative binomial, and binomial, and they can be fit in standard or zero-inflated form. I illustrate the command with an application to analysis of data from the UK Understanding Society survey on the demand for healthcare services.
文章10：intcount：用于从间隔数据拟合计数数据模型的命令
S. Pudney
摘要：在本文中，我描述了一个由社区贡献的命令intcount，它适合几种以间隔形式观察到的计数数据的回归模型之一。可用的模型为Poisson，负二项式和二项式，它们可以标准或零膨胀形式拟合。我用一个应用程序来说明该命令，该应用程序用于分析英国理解协会关于医疗保健服务需求的调查数据。
文章11：parallel: A command for parallel computing
G. G. Vega Yon and B. Quistorff
Abstract: The parallel package allows parallel processing of tasks that are not interdependent. This allows all flavors of Stata to take advantage of multiprocessor machines. Even Stata/MP users can benefit because many community-contributed programs are not automatically parallelized but could be under our framework.
文章11：parallel：用于并行计算的命令
G. G. Vega Yon and B. Quistorff
摘要：并行程序包允许并行处理不相互依赖的任务。这使Stata的所有形式都可以利用多处理器机器。甚至Stata / MP用户也可以从中受益，因为许多社区贡献的程序不会自动并行化，但可以在我们的框架下进行。
文章12：Estimation of dynamic panel threshold model using Stata
M. H. Seo, S. Kim, and Y.-J. Kim
Abstract: In this article, we develop a command, xthenreg, that implements the first-differenced generalized method of moments estimation of the dynamic panel threshold model that Seo and Shin (2016, Journal of Econometrics 195: 169–186) proposed. Furthermore, we derive the asymptotic variance formula for a kink-constrained generalized method of moments estimator of the dynamic threshold model and provide an estimation algorithm. We also propose a fast bootstrap algorithm to implement the bootstrap for the linearity test. We illustrate the use of xthenreg through a Monte Carlo simulation and an economic application.
文章12：使用Stata估计动态面板阈值模型
M. H. Seo, S. Kim, and Y.-J. Kim
摘要：在本文中，我们开发了一个命令xthenreg，该命令实现了Seo和Shin（2016，Journal of Econometrics 195：169-186）提出的动态面板阈值模型的矩估计的一阶广义方法。此外，我们推导了动态阈值模型的弯矩约束矩估计的广义方法的渐近方差公式，并提供了一种估计算法。我们还提出了一种快速自举算法，以实现用于线性测试的自举。我们通过蒙特卡洛模拟和经济应用说明了xthenreg的使用。
文章13：gidm: A command for generalized inflated discrete models
Y. Xia, Y. Zhou, and T. Cai
Abstract: In this article, we describe the gidm command for fitting generalized inflated discrete models that deal with multiple inflated values in a distribution. Based on the work of Cai, Xia, and Zhou (Forthcoming, Sociological Methods & Research: Generalized inflated discrete models: A strategy to work with multimodal discrete distributions), generalized inflated discrete models are fit via maximum likelihood estimation. Specifically, the gidm command fits Poisson, negative binomial, multinomial, and ordered outcomes with more than one inflated value. We illustrate this command through examples for count and categorical outcomes.
文章13：gidm：用于广义膨胀离散模型的命令
Y. Xia, Y. Zhou, and T. Cai
摘要：在本文中，我们描述了gidm命令，用于拟合处理分布中多个膨胀值的广义膨胀离散模型。根据蔡，夏和周的工作（即将出版的社会学方法与研究：广义膨胀离散模型：一种适用于多峰离散分布的策略），可以通过最大似然估计来拟合广义膨胀离散模型。具体而言，gidm命令适合泊松，负二项式，多项式和有序的结果，且具有多个膨胀值。我们通过计数和分类结果示例来说明此命令。
文章14：Speaking Stata: The last day of the month
N. J. Cox
Abstract: I discuss three related problems about getting the last day of the month in a new variable. Commentary ranges from the specifics of date and other functions to some generalities on developing code. Modular arithmetic belongs in every Stata user’s coding toolbox.
文章14：讲Stata：每月的最后一天
N. J. Cox
摘要：我讨论了有关在新变量中获取月份的最后一天的三个相关问题。注释的范围从日期和其他功能的细节到开发代码的一般性。模块化算法属于每个Stata用户的编码工具箱。
文章15：Review of Richard Valliant and Jill A. Dever's Survey Weights: A Step-by-Step Guide to Calculation
S. G. Heeringa
Abstract: In this article, I review the Stata Press publication Survey Weights: A Step-by-Step Guide to Calculation by Valliant and Dever (2018).
文章15：理查德·瓦利安特（Richard Valliant）和吉尔·A·德沃（Jill A.）的调查权重回顾：一个逐步计算指南
S. G. Heeringa
摘要：在本文中，我回顾了Stata Press的出版物Survey Survey Weights：Valliant and Dever的逐步计算指南（2018）。
文章16：Review of William Gould's The Mata Book: A Book for Serious Programmers and Those Who Want to Be
B. Jann
Abstract: In this article, I review The Mata Book: A Book for Serious Programmers and Those Who Want to Be, by William Gould (2018, Stata Press).
文章16：威廉·古尔德（William Gould）的《 The Mata Book：认真的程序员和想成为的人的书》评论
B. Jann
摘要：在本文中，我将回顾William Gould 撰写的《The Mata Book：A Book for认真的程序员和那些想成为的人》（2018年，Stata出版社）。

拓展性阅读

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2年，计量经济圈公众号近1000篇文章，

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