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分享 reghdfe:多维面板固定效应估计
arlionn 2019-8-7 21:50
  作者:胡雨霄 (伦敦政治经济学院)   Stata 连享会: 知乎 | 简书 | 码云 | CSDN   Stata连享会 计量专题 || 精品课程 || 推文集锦 点击查看完整推文列表   连享会计量方法专题……   实证分析中,我们经常需要控制各个维度的个体效应,以便尽可能减轻 遗漏变量 导致的偏误。在最常用的二维面板数据中,我们通常会采用 xtreg y x i.year, fe 的形式来控制 公司个体效应 和 年度效应 。然而,在有些情况下,我们需要对三维甚至更高维度的数据进行分析 (例如, 公司-年度-高管 , 省份-城市-行业-年度 ),此时,一方面要考虑估计的可行性,另一方面还需兼顾计算速度问题。 本文介绍的 reghdfe 命令可以很好地达成上述目的。 reghdfe 主要用于实现多维固定效应线性回归。该命令类似于 areg 及 xtreg,fe ,但允许引入多维固定效应。此外,该命令在运行速度方面远远优于 areg 及 xtreg , 因此倍受研究者青睐。 本文对该命令的介绍基于 A Feasible Estimator for Linear Models with Multi-Way Fixed Effects (Correia, 2016 ) 。 1. 命令的安装 ssc install reghdfe, replace ///安装命令 2. 命令的语法 该命令的具体语法如下: reghdfe depvar , absorb(absvars) 其中, depvar : 因变量 indepvars : 解释变量 absorb(absvars) :引入固定效应 可以包含多维固定效应,即 absorb (var1,var2,var3,...) 。若想保存对某变量的固定效应,则运行命令 absorb (var1,var2,FE3=var3) , 变量 FE3 将保存对 var3 的固定效应估计结果。 可以包含不同效应间的交互影响,即 absorb(var1#var2) 。 值得注意的是, reghdfe 允许定类变量 (categorical variable) 与连续性变量 (continuous variable) 进行交互,即 absorb(i.var1#c.var2) 。实证中很少引入这样的交互项。但如果对该问题感兴趣,可参考 Duflo (2014) 。 3. 命令的操作 这一部分用两个实证的例子介绍如何运用 reghdfe 。 3.1 估计双重差分的固定效应模型(DID) 该命令可用于估计双重差分的固定效应模型(DID)。过去推文 Stata: 双重差分的固定效益模型 列举了用于估计 DID 模型的三个命令: reg , areg , 以及 xtreg 。 reghdfe 也可实现同样的估计结果,而且运行速度优于其他命令。 使用的数据请参考之前推文 Stata: 双重差分的固定效应模型 。该数据模拟的情况为,政策冲击发生在 时,对照组为 ,控制组为 。模型为 。 . set obs 400 . gen firm=_n ///生成企业数量 . expand 24 . bysort firm: gen t=_n ///时间跨度设定为24个季度(6年) . gen d=(t= 14 ) . label var d "=1 if post-treatment" ///设定事件冲击发生在第14期 . gen r=rnormal() . qui sum r, d . bysort firm: gen i=(r=r(p50)) if _n== 1 . bysort firm: replace i=i if i==. _n!= 1 ///设定处理组和对照组 . drop r . label var i "=1 if treated group, =0 if untreated group" . gen e = rnormal() ///设定随机变量 . label var e "normal random variable" . gen y = 0.3 + 0.19 *i + 1.67 *d + 0.56 *i*d + e ///模型设置 首先,回顾双重差分模型的设定形式, 其中, 为分组虚拟变量(处理组=1,控制组=0); 为分期虚拟变量(政策实施后=1,政策实施前=0);交互项 表示处理组在政策实施后的效应。 与 分别为个体固定效应和时间固定效应。 具体用于估计政策冲击对公司的影响的命令如下。 gen did = i* d ///生成交互项 reghdfe y did, absorb(firm t) vce ( cluster firm) 变量 did 即为交互项,其系数为双重差分模型重点考察的处理效应。命令 absorb(firm t) 同时引入了公司固定效应以及时间固定效应。结果如下。 . reghdfe y did, absorb(firm t) vce(cluster firm) (MWFE estimator converged in 2 iterations) HDFE Linear regression Number of obs = 9 , 600 Absorbing 2 HDFE groups F( 1 , 399 ) = 175.80 Statistics robust to heteroskedasticity Prob F = 0.0000 R-squared = 0.5102 Adj R-squared = 0.4875 Within R-sq. = 0.0198 Number of clusters (firm) = 400 Root MSE = 1.0043 (Std. Err. adjusted for 400 clusters in firm) ------------------------------------------------------------------------------ | Robust y | Coef. Std. Err. t P|t| -------------+---------------------------------------------------------------- did | .5656247 .0426601 13.26 0.000 .4817581 .6494914 _cons | 1.143579 .0084565 135.23 0.000 1.126954 1.160204 ------------------------------------------------------------------------------ Absorbed degrees of freedom: -----------------------------------------------------+ Absorbed FE | Categories - Redundant = Num. Coefs | -------------+---------------------------------------| firm | 400 400 0 *| t | 24 0 24 | -----------------------------------------------------+ * = FE nested within cluster; treated as redundant for DoF computation 3.2 估计多维固定效应的线性模型(复制一篇 AER 论文) 这一小节将介绍如何运用 reghdfe 估计多维固定效应的线性模型。American Economic Review一篇文章,The Costs of Patronage: Evidence from the British Empire (Xu, 2018), 提供的可供复制的 代码 中出现了大量 reghdfe 命令。本小节介绍该作者如何用 reghdfe 命令输出其文章Table 2第六列的结果。 Source: Xu, G. (2018). The Costs of Patronage: Evidence from the British Empire. American Economic Review, 108 (11): 3170-98. 作者在这篇文章中想要探究 任命制 (patronage) 对英国 政治体系 的影响。具体于 Table2,作者意图研究社会联系(social connections) 是否会影响政府官员的工资水平。Table 2中,第六列所估计的回归为: 其中, 为政府官员 于时间 在 州执政时的对数工资水平。Stata 命令中,该变量名为 log_salary_governor_gbp 为虚拟变量(Dummy Variable),当政府官员与其上任官员存在社会联系时,该变量取1。如若不然,则取0。社会联系包括:共同祖先,贵族身份以及教育背景。Stata 命令中,该变量名为 connected 为政府官员固定效应。该部分的设置为了解决政府官员的异质性 (heterogeneity) 问题。例如,具有较强能力的政府官员更有可能建立更多的社会关系。Stata 命令中, aid 为不同官员的 unique ID 变量。 为政府官员执政时长固定效应。设置该部分是因为,执政时间的长短可能也会对社会关系产生影响。Stata 命令中, duration 为官员执政时长变量。 为控制变量。作者选用了执政者在历史上执政过的州的数目。Stata 命令中,该变量名为 no_colonies 。 为年份固定效应。该部分的设置是为了吸收执政者们在不同时期受到的共同时间冲击。Stata 命令中, year 为年份变量。 为残差。作者使用了聚类标准误的方法。 该回归的原假设为, : 社会联系 ( connected ) 与政府官员的工资水平 ( log_salary_governor_gbp ) 无关。若 connected 的系数 不显著,则不拒绝原假设。若 显著,则拒绝原假设,并可以判定社会联系对政府官员的工资水平显著相关。 用 Stata 实现该回归的命令如下。 reghdfe log_salary_governor_gbp no_colonies connected, /// absorb(aid year duration) vce ( cluster bilateral) 其中, absorb(aid year duration) 同时引入了官员固定效应、时间固定效应以及执政时长固定效应。 命令运行后的结果如下所示。数据请于AER 下载 。 . quietly use "analysis.dta" , replace . reghdfe log_salary_governor_gbp no_colonies connected, /// absorb(aid year duration) vce ( cluster bilateral) (MWFE estimator converged in 26 iterations) HDFE Linear regression Number of obs = 3,510 Absorbing 3 HDFE groups F ( 2, 1517) = 25.45 Statistics robust to heteroskedasticity Prob F = 0.0000 R-squared = 0.9255 Adj R-squared = 0.9109 Within R-sq. = 0.0978 Number of clusters (bilateral) = 1,518 Root MSE = 0.2374 (Std. Err . adjusted for 1,518 clusters in bilateral) ------------------------------------------------------------------------------ | Robust log_salary~p | Coef. Std. Err . t P|t| -------------+---------------------------------------------------------------- no_colonies | .2234767 .0347473 6.43 0.000 .1553189 .2916346 connected | .0972969 .0355508 2.74 0.006 .0275628 .1670309 _cons | 7.485619 .065766 113.82 0.000 7.356617 7.614621 ------------------------------------------------------------------------------ Absorbed degrees of freedom: -----------------------------------------------------+ Absorbed FE | Categories - Redundant = Num. Coefs | -------------+---------------------------------------| aid | 456 0 456 | year | 110 1 109 | duration | 7 1 6 ?| -----------------------------------------------------+ ? = number of redundant parameters may be higher 上述结果表明,变量 connected 的系数为 0.097, 标准误为 0.036。这说明该变量在 1% 的水平上显著大于 0 。其经济学含义为,与上一任官员存在社会联系的官员,相较于无社会联系的官员,工资水平要高出 9.7%。也就是说,官员的工资水平和其社会关系显著相关。 4.结语 这篇推文主要介绍了如何在实证中运用 reghdfe .具体而言,本推文列举了两个例子。其一,为运用该命令对 DID 模型进行估计。其二,为运用该命令进行多维固定效应线性模型的估计。 文献来源 Correia, S. (2016). Linear Models with High-Dimensional Fixed Effects: An Efficient and Feasible Estimator, Working Paper. Duflo, E. (2004). The medium run effects of educational expansion: Evidence from a large school construction program in Indonesia. Journal of Development Economics , 74(1), 163-197. Xu, G. (2018). The Costs of Patronage: Evidence from the British Empire. American Economic Review , 108 (11): 3170-98.   连享会计量方法专题……   关于我们 【 Stata 连享会(公众号:StataChina) 】由中山大学连玉君老师团队创办,旨在定期与大家分享 Stata 应用的各种经验和技巧。 公众号推文同步发布于 CSDN-Stata连享会 、 简书-Stata连享会 和 知乎-连玉君Stata专栏 。可以在上述网站中搜索关键词 Stata 或 Stata连享会 后关注我们。 点击推文底部【阅读原文】可以查看推文中的链接并下载相关资料。 Stata连享会 精品专题 || 精彩推文 联系我们 欢迎赐稿: 欢迎将您的文章或笔记投稿至 Stata连享会(公众号: StataChina) ,我们会保留您的署名;录用稿件达 五篇 以上,即可 免费 获得 Stata 现场培训 (初级或高级选其一) 资格。您也可以从 连享会选题平台 → 中选择感兴趣的题目来撰写推文。 意见和资料: 欢迎您的宝贵意见,您也可以来信索取推文中提及的程序和数据。 招募英才: 欢迎加入我们的团队,一起学习 Stata。合作编辑或撰写稿件五篇以上,即可 免费 获得 Stata 现场培训 (初级或高级选其一) 资格。 联系邮件: StataChina@163.com 往期精彩推文 Stata连享会 计量专题 || 精品课程 || 推文集锦 点击查看完整推文列表 欢迎加入Stata连享会(公众号: StataChina)
个人分类: 面板数据|124 次阅读|0 个评论
分享 Stata Journal 论文全览 (2001-2019)
arlionn 2019-8-7 08:05
作者:许梦洁 (中山大学)   Stata 连享会: 知乎 | 简书 | 码云 | CSDN Stata连享会 计量专题 || 精品课程 || 推文集锦 点击查看完整推文列表   连享会计量方法专题……       导言:   什么是 Stata Journal ? Stata Journal (SJ) 是 Stata 公司主办的期刊,聚集了全球最优秀、最勤奋的 Stata 用户,分享计量和统计方法的最新进展。其行文风格简洁明了,辅以 Stata 范例来解读复杂的计量模型背后的原理。多数 Stata Journal 上的论文都提供了新近发展的计量方法的 Stata 实现程序,是追踪和学习前沿方法的绝佳读物。   我们从哪里爬取 SJ 论文? Stata Journal 由 SAGE 出版社 出版。我们从 SAGE 网站爬取了各期 Stata Journal 的目录和 PDF 原文链接,以方便各位实时浏览和下载。   哪些 SJ 论文可以免费浏览? 2001-2015 年各期论文都可以免费在线浏览;2016 年以后的论文只有购买了 SAGE 数据库版权的学校才可以浏览。由于 SAGE 已经提供了每篇论文的 DOI (如 https://doi.org/10.1177/1536867X1601600109 ),因此,可以在 -这里- 贴入 DOI 号,浏览原文。   为何缺少 SJ 1-2,1-3 和 1-4 ? Stata Journal 的前身是 Stata Technical Bulletin ( STB ),始创于 1991 年 5 月,第一期为 STB-1。STB 虽然简洁明了,但以短文 (类似于通讯稿或随笔) 为主。为此,从 2001 年 11 月开始,由 Stata Journal 取而代之,第一期为 SJ 1-1。SJ 为季刊,SJ 3-1 表示 2003 年第一期。   如何在 Stata 界面内快速查看 SJ 论文? 只需安装连享会发布的新命令 lxh ,即可在 Stata 结果窗口中呈现 Stata Journal 单篇论文的链接,可以随时查看任何一篇 SJ 论文。详情参见 。在 Stata 命令窗口输入如下命令即可安装 lxh 命令,随后输入 lxh ,并在屏幕上点击 链接即可: . net install lxh, from (https://raw.github.com/arlionn/lxh/master/) replace 特别声明: 这些论文仅限于学术交流,请勿用于商业用途。     重要提示: 由于简书无法发布长文,这里仅能展现部分内容。 完整内容请移步至本文 CSDN 版本: Amazing!在线浏览 Stata Journal 单篇论文 (2001-2019)   2001 SJ 1-1 Patrick Royston, 2001, Flexible Parametric Alternatives to the Cox Model, and more, Stata Journal, 1(1): 1–28. William Gould, 2001, Statistical Software Certification, Stata Journal, 1(1): 29–50. J. Scott Long, Jeremy Freese, 2001, Predicted Probabilities for Count Models, Stata Journal, 1(1): 51–57. A. P. Mander, 2001, Haplotype Analysis in Population-based Association Studies, Stata Journal, 1(1): 58–75. Allen McDowell, 2001, From the Help Desk, Stata Journal, 1(1): 76–85. Nicholas J. Cox, 2001, Speaking Stata: How to Repeat Yourself without going Mad, Stata Journal, 1(1): 86–97. Roger Newson, 2001, Review of Generalized Linear Models and Extensions by Hardin and Hilbe, Stata Journal, 1(1): 98–100. Christopher F. Baum, 2001, Residual Diagnostics for Cross-section Time Series Regression Models, Stata Journal, 1(1): 101–104. Patrick Royston, 2001, Sort a List of Items, Stata Journal, 1(1): 105–106. Philippe Van Kerm, Stephen P. Jenkins, 2001, Generalized Lorenz Curves and Related Graphs: An Update for Stata 7, Stata Journal, 1(1): 107–112. 2002 SJ 2-1 Sophia Rabe-Hesketh, Anders Skrondal, Andrew Pickles, 2002, Reliable Estimation of Generalized Linear Mixed Models using Adaptive Quadrature, Stata Journal, 2(1): 1–21. Roberto G. Gutierrez, 2002, Parametric Frailty and Shared Frailty Survival Models, Stata Journal, 2(1): 22–44. Roger Newson, 2002, Parameters behind “Nonparametric” Statistics: Kendall's tau, Somers’ D and Median Differences, Stata Journal, 2(1): 45–64. A. P. Mander, 2002, Analysis of Quantitative Traits using Regression and Log-linear Modeling when Phase is unknown, Stata Journal, 2(1): 65–70. Allen McDowell, 2002, From the Help Desk: Transfer Functions, Stata Journal, 2(1): 71–85. Nicholas J. Cox, 2002, Speaking Stata: How to Move Step By: Step, Stata Journal, 2(1): 86–102. John Hendrickx, 2002, Review of Regression Models for Categorical Dependent Variables Using Stata by Long and Freese, Stata Journal, 2(1): 103–105. 2003 SJ 3-1 Christopher F. Baum, Mark E. Schaffer, Steven Stillman, 2003, Instrumental Variables and GMM: Estimation and Testing, Stata Journal, 3(1): 1–31. Germán Rodríguez, Irma Elo, 2003, Intra-class Correlation in Random-effects Models for Binary Data, Stata Journal, 3(1): 32–46. Catherine L. Saunders, D. Timothy Bishop, Jennifer H. Barrett, 2003, Sample Size Calculations for Main Effects and Interactions in Case–control Studies using Stata's nchi2 and npnchi2 Functions, Stata Journal, 3(1): 47–56. Marcelo J. Moreira, Brian P. Poi, 2003, Implementing Tests with Correct Size in the Simultaneous Equations Model, Stata Journal, 3(1): 57–70. Weihua Guan, 2003, From the Help Desk: Bootstrapped Standard Errors, Stata Journal, 3(1): 71–80. Nicholas J. Cox, Ulrich Kohler, 2003, Speaking Stata: On Structure and Shape: The Case of Multiple Responses, Stata Journal, 3(1): 81–99. John McGready, 2003, Review of a Short Introduction to Stata for Biostatistics by Hills and De Stavola, Stata Journal, 3(1): 100–104. SJ 3-2 H. Joseph Newton, Nicholas J. Cox, 2003, The Stata Journal so Far: Editors’ Report, Stata Journal, 3(2): 105–108. Roger Newson, The ALSPAC Study Team, 2003, Multiple—test Procedures and Smile Plots, Stata Journal, 3(2): 109–132. Isaías H. Salgado-Ugarte, Marco A. Pérez-Hernández, 2003, Exploring the Use of Variable Bandwidth Kernel Density Estimators, Stata Journal, 3(2): 133–147. Philippe Van Kerm, 2003, Adaptive Kernel Density Estimation, Stata Journal, 3(2): 148–156. Omar M. G. Keshk, 2003, CDSIMEQ: A Program to Implement Two-stage Probit Least Squares, Stata Journal, 3(2): 157–167. David M. Drukker, 2003, Testing for Serial Correlation in Linear Panel-data Models, Stata Journal, 3(2): 168–177. Allen McDowell, 2003, From the Help Desk: Hurdle Models, Stata Journal, 3(2): 178–184. Nicholas J. Cox, 2003, Speaking Stata: Problems with Lists, Stata Journal, 3(2): 185–202. Joanne M. Garrett, 2003, Review of Statistical Modeling for Biomedical Researchers by Dupont, Stata Journal, 3(2): 203–207. Steven Stillman, 2003, Review of Generalized Estimating Equations by Hardin and Hilbe, Stata Journal, 3(2): 208–210. SJ 15-4 H. Joseph Newton, Nicholas J. Cox, 2015, The Stata Journal Editors’ Prize 2015: Richard Williams, Stata Journal, 15(4): 901–904. Anna Chaimani, Georgia Salanti, 2015, Visualizing Assumptions and Results in Network Meta-analysis: The Network Graphs Package, Stata Journal, 15(4): 905–950. Ian R. White, 2015, Network Meta-analysis, Stata Journal, 15(4): 951–985. Ignace De Vos, Gerdie Everaert, Ilse Ruyssen, 2015, Bootstrap-based Bias Correction and Inference for Dynamic Panels with Fixed Effects, Stata Journal, 15(4): 986–1018. Giovanni Cerulli, 2015, ctreatreg: Command for Fitting Dose–response Models under Exogenous and Endogenous Treatment, Stata Journal, 15(4): 1019–1045. Charles Lindsey, Simon Sheather, 2015, Best Subsets Variable Selection in Nonnormal Regression Models, Stata Journal, 15(4): 1046–1059. Caroline Bascoul-Mollevi, Florence Castan, David Azria, Sophie Gourgou-Bourgade, 2015, EORTC QLQ-C30 Descriptive Analysis with the qlqc30 Command, Stata Journal, 15(4): 1060–1074. Maria Elena Bontempi, Irene Mammi, 2015, Implementing a Strategy to Reduce the Instrument Count in Panel GMM, Stata Journal, 15(4): 1075–1097. Patrick Royston, 2015, Estimating the Treatment Effect in a Clinical Trial Using Difference in Restricted Mean Survival Time, Stata Journal, 15(4): 1098–1117. Modesto Escobar, 2015, Studying Coincidences with Network Analysis and Other Multivariate Tools, Stata Journal, 15(4): 1118–1156. Hüseyin Tastan, 2015, Testing for Spectral Granger Causality, Stata Journal, 15(4): 1157–1166. Seth T. Lirette, 2015, Complete Automation of a Participant Characteristics Table, Stata Journal, 15(4): 1167–1173. Nicholas J. Cox, 2015, Speaking Stata: A Set of Utilities for Managing Missing Values, Stata Journal, 15(4): 1174–1185. 2016 SJ 16-1 H. Joseph Newton, Nicholas J. Cox, 2016, Announcement of the Stata Journal Editors’ Prize 2016, Stata Journal, 16(1): 1–2. H. Joseph Newton, Nicholas J. Cox, 2016, 16 and all That, Stata Journal, 16(1): 3–4. David C. Hoaglin, 2016, Regressions are Commonly Misinterpreted, Stata Journal, 16(1): 5–22. James W. Hardin, 2016, Regressions are Commonly Misinterpreted: Comments on the Article, Stata Journal, 16(1): 23–24. J. Scott Long, David M. Drukker, 2016, Regressions are Commonly Misinterpreted: Comments on the Article, Stata Journal, 16(1): 25–29. David C. Hoaglin, 2016, Regressions are Commonly Misinterpreted: A Rejoinder, Stata Journal, 16(1): 30–36. John Mullahy, 2016, Estimation of Multivariate Probit Models via Bivariate Probit, Stata Journal, 16(1): 37–51. Juan M. Villa, 2016, Diff: Simplifying the Estimation of Difference-in-differences Treatment Effects, Stata Journal, 16(1): 52–71. Patrick Royston, Willi Sauerbrei, 2016, Mfpa: Extension of mfp Using the ACD Covariate Transformation for Enhanced Parametric Multivariable Modeling, Stata Journal, 16(1): 72–87. Babak Choodari-Oskooei, Tim P. Morris, 2016, Quantifying the Uptake of user-written Commands over Time, Stata Journal, 16(1): 88–95. Alexander Plum, 2016, Bireprob: An Estimator for Bivariate Random-effects Probit Models, Stata Journal, 16(1): 96–111. Owen O'Donnell, Stephen O'Neill, Tom Van Ourti, Brendan Walsh, 2016, Conindex: Estimation of Concentration Indices, Stata Journal, 16(1): 112–138. Kai Arzheimer, Jocelyn Evans, 2016, Estimating Polling Accuracy in Multiparty Elections Using Surveybias, Stata Journal, 16(1): 139–158. Mónica Hernández-Alava, Stephen Pudney, 2016, Bicop: A Command for Fitting Bivariate Ordinal Regressions with Residual Dependence Characterized by a Copula Function and Normal Mixture Marginals, Stata Journal, 16(1): 159–184. David Lora, Israel Contador, José F. Pérez-Regadera, Agustín Gómez de la Cámara, 2016, Features of the Area under the Receiver Operating Characteristic (ROC) Curve. A Good Practice, Stata Journal, 16(1): 185–196. Miguel Sarzosa, Sergio Urzúa, 2016, Implementing Factor Models for Unobserved Heterogeneity in Stata, Stata Journal, 16(1): 197–228. Nicholas J. Cox, 2016, Speaking Stata: Truth, Falsity, Indication, and Negation, Stata Journal, 16(1): 229–236. Philip B. Ender, 2016, Review of Michael N. Mitchell's Stata for the Behavioral Sciences, Stata Journal, 16(1): 237–242. Karla Hemming, Alan Girling, 2016, A Menu-driven Facility for Power and Detectable-difference Calculations in Stepped-wedge Cluster-randomized Trials, Erratum, Stata Journal, 16(1): 243–243. SJ 16-2 Ben Jann, 2016, Creating LaTeX Documents from within Stata using Texdoc, Stata Journal, 16(2): 245–263. Ben Jann, 2016, Assessing Inequality Using Percentile Shares, Stata Journal, 16(2): 264–300. Xinling Xu, James W. Hardin, 2016, Regression Models for Bivariate Count Outcomes, Stata Journal, 16(2): 301–315. Ying Xu, Paul Milligan, Edmond J. Remarque, Yin Bun Cheung, 2016, Implementing Weighted-average Estimation of Substance Concentration Using Multiple Dilutions, Stata Journal, 16(2): 316–330. Matias D. Cattaneo, Rocío Titiunik, Gonzalo Vazquez-Bare, 2016, Inference in Regression Discontinuity Designs under Local Randomization, Stata Journal, 16(2): 331–367. Joseph V. Terza, 2016, Simpler Standard Errors for Two-stage Optimization Estimators, Stata Journal, 16(2): 368–385. Marco Savegnago, 2016, Igmobil: A Command for Intergenerational Mobility Analysis in Stata, Stata Journal, 16(2): 386–402. Nicolai T. Borgen, 2016, Fixed Effects in Unconditional Quantile Regression, Stata Journal, 16(2): 403–415. Stephan Huber, Christoph Rust, 2016, Calculate Travel Time and Distance with Openstreetmap Data Using the Open Source Routing Machine (OSRM), Stata Journal, 16(2): 416–423. Tamara Burdisso, Máximo Sangiácomo, 2016, Panel Time Series: Review of the Methodological Evolution, Stata Journal, 16(2): 424–442. Suzie Cro, Tim P. Morris, Michael G. Kenward, James R. Carpenter, 2016, Reference-based Sensitivity Analysis via Multiple Imputation for Longitudinal Trials with Protocol Deviation, Stata Journal, 16(2): 443–463. Jean-François Hamel, Véronique Sébille, Gaëlle Challet-Bouju, Jean-Benoit Hardouin, 2016, Partial Credit Model: Estimations and Tests of Fit with Pcmodel, Stata Journal, 16(2): 464–481. Kenneth Houngbedji, 2016, Abadie's Semiparametric Difference-in-differences Estimator, Stata Journal, 16(2): 482–490. Nicholas J. Cox, 2016, Speaking Stata: Multiple bar Charts in Table form, Stata Journal, 16(2): 491–510. Clyde Schechter, 2016, Review of Christopher F. Baum's An Introduction to Stata Programming, Second Edition, Stata Journal, 16(2): 511–516. Christopher F. Baum, Sebastiaan Bibo, 2016, Stata Tip 126: Handling Irregularly Spaced high-frequency Transactions Data, Stata Journal, 16(2): 517–520. SJ 16-3 Michael Keane, Timothy Neal, 2016, The Keane and Runkle Estimator for Panel-data Models with Serial Correlation and Instruments that are not Strictly Exogenous, Stata Journal, 16(3): 523–549. Oleg Badunenko, Pavlo Mozharovskyi, 2016, Nonparametric Frontier Analysis Using Stata, Stata Journal, 16(3): 550–589. Brendan Halpin, 2016, Multiple Imputation for Categorical Time Series, Stata Journal, 16(3): 590–612. Keisuke Kondo, 2016, Hot and Cold Spot Analysis Using Stata, Stata Journal, 16(3): 613–631. Kenneth L. Simons, 2016, A Sparser, Speedier Reshape, Stata Journal, 16(3): 632–649. Andreas Andersen, Andreas Rieckmann, 2016, Using mi Impute Chained to fit ANCOVA Models in Randomized Trials with Censored Dependent and Independent Variables, Stata Journal, 16(3): 650–661. Andrew Q. Philips, Amanda Rutherford, Guy D. Whitten, 2016, Dynsimpie: A Command to Examine Dynamic Compositional Dependent Variables, Stata Journal, 16(3): 662–677. Theodore G. Karrison, 2016, Versatile Tests for Comparing Survival Curves Based on Weighted Log-rank Statistics, Stata Journal, 16(3): 678–690. Juan Manuel Ramos-Goñi, Yolanda Ramallo-Fariña, 2016, Eq5dds: A Command to Analyze the Descriptive System of EQ-5D Quality-of-life Instrument, Stata Journal, 16(3): 691–701. Angel Cronin, Lu Tian, Hajime Uno, 2016, Strmst2 and Strmst2pw: New Commands to Compare Survival Curves Using the Restricted Mean Survival time, Stata Journal, 16(3): 702–716. Anil Alpman, 2016, Implementing Rubin's Alternative Multiple-imputation Method for Statistical Matching in Stata, Stata Journal, 16(3): 717–739. Tomás Del Barrio Castro, Andrii Bodnar, Andreu Sansó, 2016, The Lag-length Selection and Detrending Methods for HEGY Seasonal Unit-root Tests Using Stata, Stata Journal, 16(3): 740–760. Michael P. Babington, Javier Cano-Urbina, 2016, A Test for Exogeneity in the Presence of Nonlinearities, Stata Journal, 16(3): 761–777. Michael R. M. Abrigo, Inessa Love, 2016, Estimation of Panel Vector Autoregression in Stata, Stata Journal, 16(3): 778–804. Nicholas J. Cox, 2016, Speaking Stata: Shading Zones on Time Series and Other Plots, Stata Journal, 16(3): 805–812. SJ 16-4 H. Joseph Newton, Nicholas J. Cox, 2016, The Stata Journal Editors’ Prize 2016: Patrick Royston, Stata Journal, 16(4): 815–825. David M. Drukker, 2016, A Generalized Regression-adjustment Estimator for Average Treatment Effects from Panel Data, Stata Journal, 16(4): 826–836. Ben Jann, 2016, Estimating Lorenz and Concentration Curves, Stata Journal, 16(4): 837–866. Constantin Ruhe, 2016, Estimating Survival Functions after Stcox with Time-varying Coefficients, Stata Journal, 16(4): 867–879. Odile Sauzet, Maren Kleine, 2016, Distributional Estimates for the Comparison of Proportions of a Dichotomized Continuous Outcome, Stata Journal, 16(4): 880–899. Miguel Manjón, Juan Mañez, 2016, Production Function Estimation in Stata Using the Ackerberg–Caves–Frazer Method, Stata Journal, 16(4): 900–916. Nick Guenther, Matthias Schonlau, 2016, Support Vector Machines, Stata Journal, 16(4): 917–937. E. F. Haghish, 2016, Rethinking Literate Programming in Statistics, Stata Journal, 16(4): 938–963. E. F. Haghish, 2016, Markdoc: Literate Programming in Stata, Stata Journal, 16(4): 964–988. Hannah Bower, Michael J. Crowther, Paul C. Lambert, 2016, Strcs: A Command for Fitting Flexible Parametric Survival Models on the Log-hazard Scale, Stata Journal, 16(4): 989–1012. Sebastian Kripfganz, 2016, Quasi–maximum Likelihood Estimation of Linear Dynamic Short-T panel-data Models, Stata Journal, 16(4): 1013–1038. Javier Alejo, Anil Bera, Gabriel Montes-Rojas, Antonio Galvao, Zhijie Xiao, 2016, Tests for Normality Based on the Quantile-mean Covariance, Stata Journal, 16(4): 1039–1057. Nicholas J. Cox, 2016, Speaking Stata: Letter Values as Selected Quantiles, Stata Journal, 16(4): 1058–1071. 2017 SJ 17-1 H. Joseph Newton, Nicholas J. Cox, 2017, Announcement of the Stata Journal Editors’ Prize 2017, Stata Journal, 17(1): 1–2. Ben Jann, 2017, Creating HTML or Markdown Documents from within Stata using Webdoc, Stata Journal, 17(1): 3–38. Mustafa U. Karakaplan, 2017, Fitting Endogenous Stochastic Frontier Models in Stata, Stata Journal, 17(1): 39–55. Donald W. K. Andrews, Wooyoung Kim, Xiaoxia Shi, 2017, Commands for Testing Conditional Moment Inequalities and Equalities, Stata Journal, 17(1): 56–72. Ariel Linden, 2017, A Comprehensive set of Postestimation Measures to Enrich Interrupted Time-series Analysis, Stata Journal, 17(1): 73–88. Reinhard Schunck, Francisco Perales, 2017, Within- and Between-cluster Effects in Generalized Linear Mixed Models: A Discussion of Approaches and the Xthybrid command, Stata Journal, 17(1): 89–115. Timothy Erickson, Robert Parham, Toni M. Whited, 2017, Fitting the Errors-in-variables Model Using High-order Cumulants and Moments, Stata Journal, 17(1): 116–129. Seth T. Lirette, Samantha R. Seals, Chad Blackshear, Warren May, 2017, Capturing a Stata Dataset and Releasing it into REDcap, Stata Journal, 17(1): 130–138. Federico Belotti, Gordon Hughes, Andrea Piano Mortari, 2017, Spatial Panel-data Models Using Stata, Stata Journal, 17(1): 139–180. Paul C. Lambert, 2017, The Estimation and Modeling of Cause-specific Cumulative Incidence Functions Using Time-dependent Weights, Stata Journal, 17(1): 181–207. Patrick Taffé, Mingkai Peng, Vicki Stagg, Tyler Williamson, 2017, Biasplot: A Package to Effective Plots to Assess Bias and Precision in Method Comparison Studies, Stata Journal, 17(1): 208–221. Martina Menon, Federico Perali, Nicola Tommasi, 2017, Estimation of Unit Values in Household Expenditure Surveys without Quantity Information, Stata Journal, 17(1): 222–239. Paul Corral, Daniel Kuehn, Ermengarde Jabir, 2017, Generalized Maximum Entropy Estimation of Linear Models, Stata Journal, 17(1): 240–249. SJ 17-2 James Hardin, 2017, Joseph M. Hilbe (1944–2017), Stata Journal, 17(2): 251–252. Eric J. Daza, Michael G. Hudgens, Amy H. Herring, 2017, Estimating Inverse-probability Weights for Longitudinal Data with Dropout or Truncation: The Xtrccipw Command, Stata Journal, 17(2): 253–278. Demetris Christodoulou, 2017, Heuristic Criteria for Selecting an Optimal Aspect Ratio in a Two-variable Line Plot, Stata Journal, 17(2): 279–313. Demetris Christodoulou, Vasilis Sarafidis, 2017, Regression Clustering for Panel-data Models with Fixed Effects, Stata Journal, 17(2): 314–329. Robert L. Grant, Bob Carpenter, Daniel C. Furr, Andrew Gelman, 2017, Introducing the StataStan Interface for Fast, Complex Bayesian Modeling Using Stan, Stata Journal, 17(2): 330–342. Robert L. Grant, Daniel C. Furr, Bob Carpenter, Andrew Gelman, 2017, Fitting Bayesian item response models in Stata and Stan, Stata Journal, 17(2): 343–357. Andrea Discacciati, Matteo Bottai, 2017, Instantaneous Geometric Rates via Generalized Linear Models, Stata Journal, 17(2): 358–371. Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell, Rocío Titiunik, 2017, Rdrobust: Software for Regression-discontinuity Designs, Stata Journal, 17(2): 372–404. Patrick Royston, 2017, A Combined Test for a Generalized Treatment Effect in Clinical Trials with a Time-to-event Outcome, Stata Journal, 17(2): 405–421. Giovanni Cerulli, 2017, Estimating Responsiveness Scores Using Rscore, Stata Journal, 17(2): 422–441. Tamás Bartus, 2017, Multilevel Multiprocess Modeling with Gsem, Stata Journal, 17(2): 442–461. Sarwar Islam Mozumder, Mark J. Rutherford, Paul C. Lambert, 2017, A Flexible Parametric Competing-risks Model Using a Direct Likelihood Approach for the Cause-specific Cumulative Incidence Function, Stata Journal, 17(2): 462–489. Jinjing Li, 2017, Rate Decomposition for Aggregate Data Using Das Gupta's Method, Stata Journal, 17(2): 490–502. Eva Lorenz, Sabine Gabrysch, 2017, Covariate-constrained Randomization Routine for Achieving Baseline Balance in Cluster-randomized Trials, Stata Journal, 17(2): 503–510. Roger B. Newson, 2017, Stata Tip 127: Use Capture Noisily Groups, Stata Journal, 17(2): 511–514. SJ 17-3 Mario Cruz-Gonzalez, Iván Fernández-Val, Martin Weidner, 2017, Bias Corrections for Probit and Logit Models with Two-way Fixed Effects, Stata Journal, 17(3): 517–545. Brendan Halpin, 2017, SADI: Sequence Analysis Tools for Stata, Stata Journal, 17(3): 546–572. Rachael A. Hughes, Michael G. Kenward, Jonathan A. C. Sterne, Kate Tilling, 2017, Analyzing Repeated Measurements while Accounting for Derivative Tracking, Varying Within-subject Variance, and Autocorrelation: The Xtmixediou Command, Stata Journal, 17(3): 573–599. Germán Rodríguez, 2017, Literate Data Analysis with Stata and Markdown, Stata Journal, 17(3): 600–618. Patrick Royston, 2017, Model Selection for Univariable Fractional Polynomials, Stata Journal, 17(3): 619–629. Simon Heß, 2017, Randomization Inference with Stata: A Guide and Software, Stata Journal, 17(3): 630–651. Alvaro Carril, 2017, Dealing with Misfits in Random Treatment Assignment, Stata Journal, 17(3): 652–667. Morten W. Fagerland, David W. Hosmer, 2017, How to Test for Goodness of Fit in Ordinal Logistic Regression Models, Stata Journal, 17(3): 668–686. Daniele Pacifico, Felix Poege, 2017, Estimating Measures of Multidimensional Poverty with Stata, Stata Journal, 17(3): 687–703. Jesús Otero, Jeremy Smith, 2017, Response Surface Models for OLS and GLS Detrending-based Unit-root Tests in Nonlinear ESTAR Models, Stata Journal, 17(3): 704–722. Charles F. Manski, Max Tabord-Meehan, 2017, Evaluating the Maximum MSE of Mean Estimators with Missing Data, Stata Journal, 17(3): 723–735. Mehmet F. Dicle, John D. Levendis, 2017, Technical Financial Analysis Tools for Stata, Stata Journal, 17(3): 736–747. Daniel Bischof, 2017, New Graphic Schemes for Stata: Plotplain and Plottig, Stata Journal, 17(3): 748–759. Nicholas J. Cox, 2017, Speaking Stata: Tables as Lists: The Groups Command, Stata Journal, 17(3): 760–773. Luca J. Uberti, 2017, Stata Tip 128: Marginal Effects in Log-transformed Models: A Trade Application, Stata Journal, 17(3): 774–778. SJ 17-4 H. Joseph Newton, Nicholas J. Cox, 2017, The Stata Journal Editors’ Prize 2017: Ben Jann, Stata Journal, 17(4): 781–785. Yinghui Wei, Patrick Royston, 2017, Reconstructing Time-to-event Data from Published Kaplan–Meier Curves, Stata Journal, 17(4): 786–802. Giovanni Cerulli, 2017, Identification and Estimation of Treatment Effects in the Presence of (Correlated) Neighborhood Interactions: Model and Stata Implementation via Ntreatreg, Stata Journal, 17(4): 803–833. Sebastian Galiani, Brian Quistorff, 2017, The Synth_Runner Package: Utilities to Automate Synthetic Control Estimation Using Synth, Stata Journal, 17(4): 834–849. Barbara Rossi, Matthieu Soupre, 2017, Implementing Tests for Forecast Evaluation in the Presence of Instabilities, Stata Journal, 17(4): 850–865. Matthias Schonlau, Nick Guenther, Ilia Sucholutsky, 2017, Text Mining with n-gram Variables, Stata Journal, 17(4): 866–881. Kerui Du, 2017, Econometric Convergence Test and Club Clustering Using Stata, Stata Journal, 17(4): 882–900. Guangwei Zhu, Zaichao Du, Juan Carlos Escanciano, 2017, Automatic Portmanteau Tests with Applications to Market Risk Management, Stata Journal, 17(4): 901–915. Joseph V. Terza, 2017, Two-stage Residual Inclusion Estimation: A Practitioners Guide to Stata Implementation, Stata Journal, 17(4): 916–938. Joseph V. Terza, 2017, Causal Effect Estimation and Inference Using Stata, Stata Journal, 17(4): 939–961. Sylvain Weber, Martin Péclat, 2017, A Simple Command to Calculate Travel Distance and Travel Time, Stata Journal, 17(4): 962–971. Luciano Lopez, Sylvain Weber, 2017, Testing for Granger Causality in Panel Data, Stata Journal, 17(4): 972–984. Jesús Otero, Christopher F. Baum, 2017, Response Surface Models for the Elliott, Rothenberg, and Stock Unit-root Test, Stata Journal, 17(4): 985–1002. Giovanni Nattino, Stanley Lemeshow, Gary Phillips, Stefano Finazzi, Guido Bertolini, 2017, Assessing the Calibration of Dichotomous Outcome Models with the Calibration Belt, Stata Journal, 17(4): 1003–1014. Mattia Cattaneo, Paolo Malighetti, Daniele Spinelli, 2017, Estimating Receiver Operative Characteristic Curves for Time-dependent Outcomes: The Stroccurve Package, Stata Journal, 17(4): 1015–1023. 2018 SJ 18-1 H. Joseph Newton, Nicholas J. Cox, 2018, Announcement of the Stata Journal Editors’ Prize 2018, Stata Journal, 18(1): 1–2. Patrick Royston, 2018, Power and sample-size analysis for the Royston–Parmar combined test in clinical trials with a time-to-event outcome, Stata Journal, 18(1): 3–21. Jesús Otero, Christopher F. Baum, 2018, Unit-root Tests Based on Forward and Reverse Dickey–Fuller Regressions, Stata Journal, 18(1): 22–28. Bastien Perrot, Emmanuelle Bataille, Jean-Benoit Hardouin, 2018, validscale: A Command to Validate Measurement Scales, Stata Journal, 18(1): 29–50. Laura A. Gray, Mónica Hernández Alava, 2018, A Command for Fitting Mixture Regression Models for Bounded Dependent Variables Using the Beta Distribution, Stata Journal, 18(1): 51–75. Jesse Wursten, 2018, Testing for Serial Correlation in Fixed-effects Panel Models, Stata Journal, 18(1): 76–100. Carlo Schwarz, 2018, Ldagibbs: A Command for Topic Modeling in Stata Using Latent Dirichlet Allocation, Stata Journal, 18(1): 101–117. Martin Eckhoff Andresen, 2018, Exploring Marginal Treatment Effects: Flexible Estimation Using Stata, Stata Journal, 18(1): 118–158. Oscar Barriga Cabanillas, Jeffrey D. Michler, Aleksandr Michuda, Emilia Tjernström, 2018, Fitting and Interpreting Correlated Random-coefficient Models Using Stata, Stata Journal, 18(1): 159–173. Jonathan A. Cook, Ashish Rajbhandari, 2018, Heckroccurve: ROC Curves for Selected Samples, Stata Journal, 18(1): 174–183. Helmut Herwartz, Simone Maxand, Fabian H. C. Raters, Yabibal M. Walle, 2018, Panel Unit-root Tests for Heteroskedastic Panels, Stata Journal, 18(1): 184–196. Matthew S. Gillman, 2018, Some Commands to Help Produce Rich Text Files from Stata, Stata Journal, 18(1): 197–205. Fernando Rios-Avila, Gustavo Canavire-Bacarreza, 2018, Standard-error Correction in Two-stage Optimization Models: A Quasi–maximum Likelihood Estimation Approach, Stata Journal, 18(1): 206–222. Daniel Gallacher, Felix Achana, 2018, Assessing the Health Economic Agreement of Different Data Sources, Stata Journal, 18(1): 223–233. Matias D. Cattaneo, Michael Jansson, Xinwei Ma, 2018, Manipulation Testing Based on Density Discontinuity, Stata Journal, 18(1): 234–261. Nicholas J. Cox, 2018, Speaking Stata: Logarithmic Binning and Labeling, Stata Journal, 18(1): 262–286. Alexander Koplenig, 2018, Stata Tip 129: Efficiently Processing Textual Data with Stata's New Unicode Features, Stata Journal, 18(1): 287–289. SJ 18-2 Richard Williams, Paul D. Allison, Enrique Moral-Benito, 2018, Linear Dynamic Panel-data Estimation Using Maximum Likelihood and Structural Equation Modeling, Stata Journal, 18(2): 293–326. Susan Donath, 2018, Baselinetable: A Command for Creating one- and Two-way Tables of Summary Statistics, Stata Journal, 18(2): 327–344. Marshall A. Taylor, 2018, Simulating the Central Limit Theorem, Stata Journal, 18(2): 345–356. John A. Gallis, Fan Li, Hengshi Yu, Elizabeth L. Turner, 2018, Cvcrand and Cptest: Commands for Efficient Design and Analysis of Cluster Randomized Trials Using Constrained Randomization and Permutation Tests, Stata Journal, 18(2): 357–378. Mehmet F. Dicle, Betul Dicle, 2018, Content Analysis: Frequency Distribution of Words, Stata Journal, 18(2): 379–386. Christiaan H. Righolt, Salaheddin M. Mahmud, 2018, Attrition Diagrams for Clinical Trials and Meta-analyses in Stata, Stata Journal, 18(2): 387–394. Mónica Hernández-Alava, Stephen Pudney, 2018, Eq5Dmap: A Command for Mapping between EQ-5D-3L and EQ-5D-5L, Stata Journal, 18(2): 395–415. Michael J. Grayling, James M. S. Wason, Adrian P. Mander, 2018, Group Sequential Clinical Trial Designs for Normally Distributed Outcome Variables, Stata Journal, 18(2): 416–431. Noori Akhtar-Danesh, 2018, Qfactor: A Command for Q-methodology Analysis, Stata Journal, 18(2): 432–446. Chang Hyung Lee, Douglas G. Steigerwald, 2018, Inference for Clustered Data, Stata Journal, 18(2): 447–460. Fausto Pacicco, Luigi Vena, Andrea Venegoni, 2018, Event Study Estimations Using Stata: The Estudy Command, Stata Journal, 18(2): 461–476. Ying Xu, Yin Bun Cheung, 2018, Frailty Models and Frailty-mixture Models for Recurrent Event Times: Update, Stata Journal, 18(2): 477–484. Ariel Linden, 2018, Review of Tenko Raykov and George Marcoulides's a Course in Item Response Theory and Modeling with Stata, Stata Journal, 18(2): 485–488. SJ 18-3 Ben Jann, 2018, Customizing Stata Graphs made Easy (Part 1), Stata Journal, 18(3): 491–502. Mark D. Chatfield, 2018, Graphing Each Individual's Data over Time, Stata Journal, 18(3): 503–516. Vincenzo Verardi, Catherine Vermandele, 2018, Univariate and Multivariate Outlier Identification for Skewed or Heavy-Tailed Distributions, Stata Journal, 18(3): 517–532. Shawna K. Metzger, Benjamin T. Jones, 2018, Mstatecox: A Package for Simulating Transition Probabilities from Semiparametric Multistate Survival Models, Stata Journal, 18(3): 533–563. Jordy Meekes, Wolter H. J. Hassink, 2018, Flowbca: A Flow-Based Cluster Algorithm in Stata, Stata Journal, 18(3): 564–584. Jan Ditzen, 2018, Estimating Dynamic Common-Correlated Effects in Stata, Stata Journal, 18(3): 585–617. Gabriele Rovigatti, Vincenzo Mollisi, 2018, Theory and Practice of Total-Factor Productivity Estimation: The Control Function Approach using Stata, Stata Journal, 18(3): 618–662. Damian Clarke, Benjamín Matta, 2018, Practical Considerations for Questionable IVs, Stata Journal, 18(3): 663–691. Long Hong, Guido Alfani, Chiara Gigliarano, Marco Bonetti, 2018, Giniinc: A Stata Package for Measuring Inequality from Incomplete Income and Survival Data, Stata Journal, 18(3): 692–715. Anna Chaimani, Dimitris Mavridis, Georgia Salanti, Julian P. T. Higgins, Ian R. White, 2018, Allowing for Informative Missingness in Aggregate Data Meta-Analysis with Continuous or Binary Outcomes: Extensions to Metamiss, Stata Journal, 18(3): 716–740. Nicholas J. Cox, 2018, Speaking Stata: From Rounding to Binning, Stata Journal, 18(3): 741–754. Nicholas J. Cox, 2018, Stata Tip 130: 106610 and All That: Date Variables that Need to be Fixed, Stata Journal, 18(3): 755–757. SJ 18-4 H. Joseph Newton, Nicholas J. Cox, 2018, The Stata Journal Editors’ Prize 2018: Federico Belotti, Stata Journal, 18(4): 761–764. Ben Jann, 2018, Color Palettes for Stata Graphics, Stata Journal, 18(4): 765–785. Ben Jann, 2018, Customizing Stata Graphs Made Easy (Part 2), Stata Journal, 18(4): 786–802. Liyang Sun, 2018, Implementing Valid Two-Step Identification-Robust Confidence Sets for Linear Instrumental-Variables Models, Stata Journal, 18(4): 803–825. Michael J. Grayling, Adrian P. Mander, 2018, Calculations Involving the Multivariate Normal and Multivariate t Distributions with and without Truncation, Stata Journal, 18(4): 826–843. Raffaele Grotti, Giorgio Cutuli, 2018, Xtpdyn: A Community-Contributed Command for Fitting Dynamic Random-Effects Probit Models with Unobserved Heterogeneity, Stata Journal, 18(4): 844–862. Sébastien Fontenay, 2018, sdmxuse: Command to Import Data from Statistical Agencies using the SDMX Standard, Stata Journal, 18(4): 863–870. Daniel Klein, 2018, Implementing a General Framework for Assessing Interrater Agreement in Stata, Stata Journal, 18(4): 871–901. Soren Jordan, Andrew Q. Philips, 2018, Cointegration Testing and Dynamic Simulations of Autoregressive Distributed Lag Models, Stata Journal, 18(4): 902–923. Shawn Bauldry, Jun Xu, Andrew S. Fullerton, 2018, Gencrm: A New Command for Generalized Continuation-Ratio Models, Stata Journal, 18(4): 924–936. Denis Chetverikov, Dongwoo Kim, Daniel Wilhelm, 2018, Nonparametric Instrumental-Variable Estimation, Stata Journal, 18(4): 937–950. Xiaoqing Ye, Yixiao Sun, 2018, Heteroskedasticity- and Autocorrelation-robust F and t Tests in Stata, Stata Journal, 18(4): 951–980. Nicholas J. Cox, Clyde B. Schechter, 2018, Speaking Stata: Seven Steps for Vexatious String Variables, Stata Journal, 18(4): 981–994. Patrick Royston, 2018, Power and Sample-Size Analysis for the Royston–Parmar Combined Test in Clinical Trials with a Time-to-Event Outcome: Correction and Program Update, Stata Journal, 18(4): 995–996. 2019 SJ 19-1 H. Joseph Newton, Nicholas J. Cox, 2019, Change of publisher: The Stata Journal is now published by SAGE Publishing, Stata Journal, 19(1): 1–1. H. Joseph Newton, Nicholas J. Cox, 2019, Announcement of the Stata Journal Editors’ Prize 2019, Stata Journal, 19(1): 2–3. David Roodman, Morten Ørregaard Nielsen, James G. MacKinnon, Matthew D. Webb, 2019, Fast and wild: Bootstrap inference in Stata using boottest, Stata Journal, 19(1): 4–60. E. F. Haghish, 2019, Seamless interactive language interfacing between R and Stata, Stata Journal, 19(1): 61–82. E. F. Haghish, 2019, On the importance of syntax coloring for teaching statistics, Stata Journal, 19(1): 83–86. Alfonso Sánchez-Peñalver, 2019, Estimation methods in the presence of corner solutions, Stata Journal, 19(1): 87–111. Maciej Jakubowski, Artur Pokropek, 2019, piaactools: A program for data analysis with PIAAC data, Stata Journal, 19(1): 112–128. Carlo Schwarz, 2019, lsemantica: A command for text similarity based on latent semantic analysis, Stata Journal, 19(1): 129–142. Stanislav Kolenikov, 2019, Updates to the ipfraking ecosystem, Stata Journal, 19(1): 143–184. Constantin Ruhe, 2019, Bootstrap pointwise confidence intervals for covariate-adjusted survivor functions in the Cox model, Stata Journal, 19(1): 185–199. Mehmet F. Dicle, 2019, Candle charts for financial technical analysis, Stata Journal, 19(1): 200–209. Matias D. Cattaneo, Rocío Titiunik, Gonzalo Vazquez-Bare, 2019, Power calculations for regression-discontinuity designs, Stata Journal, 19(1): 210–245. Nicholas J. Cox, Clyde B. Schechter, 2019, Speaking Stata: How best to generate indicator or dummy variables, Stata Journal, 19(1): 246–259. ——, 2019, Announcements, Stata Journal, 19(1): 261–261. ——, 2019, Stata Conference advertisement: Journal announcement from StataCorp, Stata Journal, 19(1): NP1–NP1. ——, 2019, The Stata Journal back issues: Journal announcement from StataCorp, Stata Journal, 19(1): NP2–NP2.   欢迎加入Stata连享会(公众号: StataChina)
个人分类: stata资源|98 次阅读|0 个评论
分享 Stata现场班(寒假班:每年1月中旬;暑假班:每年7月中旬)
arlionn 2017-4-29 16:46
Stata暑假现场班即将开班(授课:连玉君,2017.7.12-20,北京) 核心内容: 断点回归分析(RDD);合成控制法(Synthesis Control Methods);面板门槛模型;面板VAR模型;自科标书的撰写;一篇经典论文的Stata重现(Journal of Finance) 等. 初级班(2017年7月12-15日,四天,此链接长期有效): http://www.peixun.net/view/307.html 高级班(2017年7月17-20日,四天,此链接长期有效): http://www.peixun.net/view/308.html PDF课程大纲: http://pan.baidu.com/share/link?shareid=1057481064uk=604047888 烦请给位转发给有需要的同学和老师,多谢!
个人分类: stata|154 次阅读|0 个评论
分享 stata review
accumulation 2014-12-19 22:39
Basic Commands: •gen y=x^2 •gen y=x*z •gen y=log(x) •gen y=sum(x) •egen y=sum(x) •replace y=x •replace y=exp(y) •tab x •tab x y •su y •reg y x1 x2 x3 if x4==1 •reg y x1 x2 x3, robust •pre yhat, xb •pre uhat, res •test x1 x2 •test (x1=1)(x2=0)(x3=0) •test x1=x2 •test x1=3*x2
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分享 stata笔记—异方差的检验
accumulation 2014-12-17 20:07
We have data of y,x1,x2,x3,x4 (1) reg; (2) BP-test; (3) White test; Solve: (1) reg y,x1,x2,x3,x4; (2) estat hettest rhs;(rhs=right hand side) or estat hettest x1 x2 x3 x4; estat hettest normal normal: 对 y 的拟合值进行 BP 检验; (3) estat imtest , white; (2) 、 (3) 的其它检验方法:利用 BP-test 与 White test 的定义进行检验;其中, BP-test 第三步对残差平方的检验可以有 F 检验与 LM 检验两种方法; 按步骤的解法: (2)reg y,x1,x2,x3,x4; predict uhat,res gen uhat2=uhat^2 reg uhat2 x1 x2 x3 x4 记下 R-squared ,计算 F 值;或者直接 test x1 x2 x3 x4 ; 或者进行 LM 检验; (3)White test 考虑了平方项引起的异方差; reg y,x1,x2,x3,x4; predict uhat,res gen uhat2=uhat^2 predict yhat xb gen yhat2=yhat^2 reg uhat2 yhat yhat2 记下 R-squared ,计算 F 值;或者进行 LM 检验。
12 次阅读|0 个评论
分享 Stata中_n 和 _N的用法
我叫小刚 2013-12-20 07:03
_n 和 _N 是两个stata的系统变量,_n指的是现在观测值的行数,_N指的是数据中观测值的总数 _n常用来生成每个观测值的唯一编码: gen code=_n 用这个方法可以产生每个观测值的对应值,第一个观测值的_n==1,最后一个观测值的_n==N 或者可以用来产生和邻近观测值相关的变量(也叫下标subscripting) gen gdplag=gdp 产生gdplag变量,这个变量等于gdp的前一个观测值 gen gdpgrowth=(gdp /gdp - 1)*100 产生gdpgrowth变量,等于gdp的增长率 注意这里gdplag和gdpgrowth的第一个变量会缺失,因为观测值 对于_n==1的观测值来说不存在。 Make sure, however, that you refer to the right neighbor! For example, if you are calculating the growth rate of variable gdp between 1999 and 2000, gdp must be in order such that the gdp subscripted by is the gdp for 1999. This is easily addressed by invoking the -sort- command, “sort year,” before generating the growth rate variable. There is another complication, however, when you are calculating this for different groups of observations, say by country. Will “sort country year” before generating the variable suffice? No. Why? Because the for the first observation of country B refers to the last observation of country A. Here is where Super -bysort- comes to the rescue: bysort country year: gen gdplag=gdp bysort country year: gen gdpgrowth=(gdp /gdp - 1)*100 // Another syntax for bysort is: by country (year), sort: … _n may also be used to keep the nth observation by group: bysort householdid: keep if _n==1 /* keeps the first observation for each householdid */ Big brother _N, on the other hand, may be used to generatea variable that contains the number of observations by group: bysort householdid: gen householdsize=_N /* generates the variable householdsize, which is equal to the number of observationsfor each householdid. */ What we have illustrated above are just a few examples to showcase the potential of underscore variables _n and _N. For sure, you will find other uses of _n and _N. Another underscore variable is the beautiful number π, which, as you would’ve guessed, is written as _pi.
个人分类: STATA|0 个评论
分享 ARIMA STATA Command Notes
aliehs 2013-8-8 04:14
* Recreating Harold Clarke's analysis of the Falkland Islands effects * on UK political economy. We'll start with some housekeeping and then work * with the univariate ARIMA analysis of the dependent variable, logged government * popularity (govpopl). clear cd "C:\Users\ak\Dropbox\Conferences Summer School\Essex Summer School\Lab Files for Dynamic Modeling Essex 2013" use "br7983.dta", clear * Remember that by default STATA assumes it is working with cross sectional data. STATA also * has it's own date variables. So, in order to use some of the time series commands in STATA, * we will need to create an appropriate time variable and tsset it. An easy way to start * doing this is to create a counter-type variable in STATA. (在产生一个新变量。最后一列) egen counter=fill(1 2 3) * Dr. Clarke's data begin in July 1979. In STATA January 1960 is the zero month. To get a better * handle on how STATA deals with dates, we can call use the help command: help dates * So, using a little math (19*12+6=233), we can figure out that July 1979 is month number 234 in STATA's * world (给月份设定正确的counter) gen Month=counter+233 * We can now format this variable (之前的format是7901这种格式,这里从counter又转为month的格式) format Month %tm * To see check if we did this right, we can list both variables: list month Month * And we can tsset this month: tsset Month * The first step is to determine whether or not the series is stationary. We know from lectures * that we have discussed doing this through the use of graphs and the ACF. * (twoway 的意思是:twoway is a family of plots, all of which fit on numeric y and x scales.The leading graph is * optional. If the first (or only) plot is scatter,you may omit twoway as well, and then the syntax is and the * same applies to line. The other plottypes must be preceded by twoway.) twoway line govpopl Month twoway line govpop Month twoway line govpopl Month || line govpop Month * We introduced the corrgram, ac, and pac commands in the program titled MA_AR_diagnostics corrgram govpopl ac govpopl pac govpopl * If we use Enders recommended procedure for diagnosing unit roots, we start with a fully-specified * (既有,trend, 又有,constant, 但为什么是lags(3)?) * Augmented Dickey-Fuller model. The "regress option" tells Stata that we want to see the * results of the diagnostic regression: dfuller govpopl, trend lags(3) regress * Reading down the regression table, we see that the model is estimated with the dependent variable * as "D.govpopl" which means the differenced version of the dependent variable. * (dickey-fuller就是test differenced level 的嘛!) * The coefficient on "L1." is statistically indistinguishable from zero (p=20%.It's not significantly different from zero * or it's not significant, so we can't reject the Ho: gemma=0)so gemma doesn't equal to 0) * This is the critical gamma parameter and * an early indication of a unit root process. So, going to Enders diagram, our answer to question (1) * is "yes." (Ho: gemma=0; Is gamma = zero? yes) * Moving to question 2. (test for the presence of trend!) * Stata doesn't have a canned procedure for estimating the Phi_3 test from * Enders' diagram, but we can see that the trend variable is close to zero. (yes! 95% conf.interval: -.000753-0045549. 所以这一步是观察出来的) * As such, we can estimate the dfuller govpopl, lags(3) regress *(这一步应该是Q3) * Question 3: (这一步应该是Q4:test constant) dfuller govpopl, lags(3) noconstant regress * 为什么这里把所有的lag都去掉了? dfuller govpopl, noconstant regress * 0.896 -.0082055 .0093569 不能否定它=0,所以就有unit root! * Conclusion: Non-stationary, needs to be differenced once tsline D.govpopl dfuller D.govpopl, regress * MacKinnon approximate p-value for Z(t) = 0.0000, reject H0, no unit root. It's stationary. corrgram D.govpopl ac D.govpopl pac D.govpopl * Conclusion: MA4 and maybe MA5, so pdq=(0,1,1 or 2)(为什么不加上更多的lag?) * Now run ARIMA and look at ACF of the resulting residuals and Ljung-Box Q arima D.govpopl, ma(4 5) nocons * After STATA has estimated a model, the command predict can be used to create new variables. * In this case, we want to examine the residuals to see if they are white noise: predict govpopl_res45, residuals * 为什么要用predict residual? (predict: Obtain predictions, residuals, etc., after estimation) corrgram govpopl_res45 ac govpopl_res45 estat ic * estat: Obtain information criteria * Let's see what we get with just the MA4: arima D.govpopl, ma(4) nocons predict govpopl_res4, residuals estat ic corrgram govpopl_res4 ac govpopl_res4 * From here we can go through the same steps with the rest of the variables to be used in the analysis. tsline empxl dfuller empxl, regress corrgram D.empxl arima empxl, arima(1,1,1) predict empxlres_111, resid corrgram empxlres_111 estat ic arima empxl, arima(2,1,0) predict empxlres_210, resid corrgram empxlres_210 estat ic
个人分类: Stata|13 次阅读|0 个评论

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