Pub Date : 2023-06-01DOI: 10.1177/1536867X231175333
D. Drukker
Stata estimation commands that implement frequentist methods produce an output table that contains multiple tests and multiple confidence intervals. Presumably, the multiple tests and multiple confidence are designed to help determine which parameters are responsible for a possible rejection of the overall null hypothesis of no effect. When taken by itself, each test and each confidence interval provides valid inference about the null hypothesis of no effect for each parameter at the specified error rate. However, simultaneously using two or more of these tests or confidence intervals provides inference at an error rate that exceeds the one specified. In this article, I discuss the sotable command, which provides p-values that are adjusted for the multiple tests and a confidence band that can be used to simultaneously test multiple parameters for no effect after almost all frequentist estimation commands. I also provide an introduction to the literature on simultaneous inference.
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Pub Date : 2023-06-01DOI: 10.1177/1536867X231175272
D. Drukker, Di Liu
Inference for structural and treatment parameters while having high-dimensional covariates in the model is increasingly common. The Neyman-orthogonal (NO) estimators in Belloni, Chernozhukov, and Wei (2016, Journal of Business and Economic Statistics 34: 606–619) produce valid inferences for the parameters of interest while using generalized linear model lasso methods to select the covariates. Drukker and Liu (2022, Econometric Reviews 41: 1047–1076) extended the estimators in Belloni, Chernozhukov, and Wei (2016) by using a Bayesian information criterion stepwise method and a testing-stepwise method as the covariate selector. Drukker and Liu (2022) found a family of data-generating processes for which the NO estimator based on Bayesian information criterion stepwise produces much more reliable inferences than the lasso-based NO estimator. In this article, we describe the implementation of posw, a command for the stepwise-based NO estimator for the high-dimensional linear, logit, and Poisson models.
在模型中具有高维协变量的情况下，对结构和处理参数的推断越来越普遍。Belloni, Chernozhukov, and Wei (2016, Journal of Business and Economic Statistics 34: 606-619)的Neyman-orthogonal (NO)估计在使用广义线性模型lasso方法选择协变量的同时，对感兴趣的参数产生有效的推断。Drukker和Liu (2022, Econometric Reviews 41: 1047-1076)采用贝叶斯信息准则逐步方法和检验-逐步方法作为协变量选择器，扩展了Belloni、Chernozhukov和Wei(2016)的估计量。Drukker和Liu(2022)发现了一系列数据生成过程，其中基于贝叶斯信息准则的NO估计器逐步产生比基于laso的NO估计器更可靠的推断。在本文中，我们描述了posw的实现，posw是用于高维线性、logit和泊松模型的基于逐步的NO估计器的命令。
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Pub Date : 2023-06-01DOI: 10.1177/1536867X231175276
Laura Magazzini, G. Calzolari
In this article, we describe the xttestms command, which implements the Lagrange multiplier test proposed by Magazzini and Calzolari (2020, Econometric Reviews 39: 115–134). The test verifies the validity of the initial conditions in dynamic panel-data models, which is required for consistency of the system generalized method of moments estimator.
{"title":"A Lagrange multiplier test for the mean stationarity assumption in dynamic panel-data models","authors":"Laura Magazzini, G. Calzolari","doi":"10.1177/1536867X231175276","DOIUrl":"https://doi.org/10.1177/1536867X231175276","url":null,"abstract":"In this article, we describe the xttestms command, which implements the Lagrange multiplier test proposed by Magazzini and Calzolari (2020, Econometric Reviews 39: 115–134). The test verifies the validity of the initial conditions in dynamic panel-data models, which is required for consistency of the system generalized method of moments estimator.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44575641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1177/1536867X231175350
G. Longton
The most important change in this update is the addition of the subcommand breakdown, which issues a report on different missing values; that is, the numbers present as 1) empty strings "" if string variables are included and 2) system missing and extended missing values if numeric variables are included. This subcommand is most obviously useful as a check on the presence of extended missing values for numeric variables.
{"title":"Software Updates","authors":"G. Longton","doi":"10.1177/1536867X231175350","DOIUrl":"https://doi.org/10.1177/1536867X231175350","url":null,"abstract":"The most important change in this update is the addition of the subcommand breakdown, which issues a report on different missing values; that is, the numbers present as 1) empty strings \"\" if string variables are included and 2) system missing and extended missing values if numeric variables are included. This subcommand is most obviously useful as a check on the presence of extended missing values for numeric variables.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46768549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1177/1536867X231175265
H. Tauchmann
Besides the Wald and likelihood-ratio tests, the Lagrange multiplier test (Rao, 1948, Mathematical Proceedings of the Cambridge Philosophical Society 44: 50–57; Aitchison and Silvey, 1958, Annals of Mathematical Statistics 29: 813–828; Silvey, 1959, Annals of Mathematical Statistics 30: 389–407) is the third canonical approach to testing hypotheses after maximum likelihood estimation. While the Stata commands test and lrtest implement the first two, Stata does not have an official command for implementing the third. The community-contributed boottest package (Roodman et al., 2019, Stata Journal 19: 4–60) focuses on methods of bootstrap inference and also implements the Lagrange multiplier test functionality. In this article, I introduce the new community-contributed postestimation command lgrgtest, which allows for straightforwardly using the Lagrange multiplier test after constrained maximum-likelihood estimation. lgrgtest is intended to be compatible with all Stata estimation commands that use maximum likelihood and allow for the options constraints(), iterate(), and from(). lgrgtest can also be used after cnsreg.
除了Wald检验和似然比检验，拉格朗日乘数检验(Rao, 1948，《剑桥哲学学会数学论文集》44:50-57;艾奇逊和西尔维，1958，数理统计年鉴29:813-828;Silvey, 1959, Annals of Mathematical Statistics 30: 389-407)是继极大似然估计之后检验假设的第三个规范方法。虽然Stata命令test和lrtest实现了前两个功能，但Stata没有用于实现第三个功能的官方命令。社区贡献的引导包(Roodman et al.， 2019, Stata Journal 19:4 - 60)侧重于引导推理的方法，并实现了拉格朗日乘子测试功能。在本文中，我将介绍新的社区贡献的后估计命令lgrgtest，它允许在受限的最大似然估计之后直接使用拉格朗日乘数测试。lgrgtest旨在与所有使用最大似然的Stata估计命令兼容，并允许使用constraints()、iterate()和from()选项。Lgrgtest也可以在cnsregg之后使用。
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Pub Date : 2023-06-01DOI: 10.1177/1536867X231175253
Marius Radean
Interaction analyses are useful tools to examine complex socioeconomic outcomes in which the effect of one variable depends on the presence or values of another variable. Interaction effects capture simultaneous changes in two (or more) covariates, and their computation is especially challenging in nonlinear models. For such models, a statistically significant interaction-term coefficient does not necessarily indicate significant interactive effects. For analyses in which the interaction effect cannot be inferred from the model estimates, I introduce ginteff, a new command that automatically computes two- and three-way interaction effects. The command accommodates a large suite of estimation models and allows researchers to use either the partial derivative or the first difference to model the effect of the interacted variables.
{"title":"ginteff: A generalized command for computing interaction effects","authors":"Marius Radean","doi":"10.1177/1536867X231175253","DOIUrl":"https://doi.org/10.1177/1536867X231175253","url":null,"abstract":"Interaction analyses are useful tools to examine complex socioeconomic outcomes in which the effect of one variable depends on the presence or values of another variable. Interaction effects capture simultaneous changes in two (or more) covariates, and their computation is especially challenging in nonlinear models. For such models, a statistically significant interaction-term coefficient does not necessarily indicate significant interactive effects. For analyses in which the interaction effect cannot be inferred from the model estimates, I introduce ginteff, a new command that automatically computes two- and three-way interaction effects. The command accommodates a large suite of estimation models and allows researchers to use either the partial derivative or the first difference to model the effect of the interacted variables.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47838342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1177/1536867X231175305
J. Ditzen, Simon Reese
In this article, we introduce a new community-contributed command, xtnumfac, for estimating the number of common factors in time-series and panel datasets using the methods of Bai and Ng (2002, Econometrica 70: 191–221), Ahn and Horenstein (2013, Econometrica 81: 1203–1227), Onatski (2010, Review of Economics and Statistics 92: 1004–1016), and Gagliardini, Ossola, and Scaillet (2019, Journal of Econometrics 212: 503–521). Common factors are usually unobserved or unobservable. In time series, they influence all predictors, while in paneldata models, they influence all cross-sectional units at different degrees. Examples are shocks from oil prices, inflation, or demand or supply shocks. Knowledge about the number of factors is key for multiple econometric estimation methods, such as Pesaran (2006, Econometrica 74: 967–1012), Bai (2009, Econometrica 77: 1229–1279), Norkute et al. (2021, Journal of Econometrics 220: 416–446), and Kripfganz and Sarafidis (2021, Stata Journal 21: 659–686). This article discusses a total of 10 methods to estimate the number of common factors. Examples based on Kapetanios, Pesaran, and Reese (2021, Journal of Econometrics 221: 510–541) show that U.S. house prices are exposed to up to 10 common factors. Therefore, when one fits models with house prices as a dependent variable, the number of factors must be considered.
在本文中，我们引入了一个新的社区贡献的命令xtnumfacc，用于估计时间序列和面板数据集中的共同因子数量，使用了Bai和Ng (2002, Econometrica 70: 191-221)， Ahn和Horenstein (2013, Econometrica 81: 1203-1227)， Onatski (2010, Review of Economics and Statistics 92: 1004-1016)和Gagliardini, Ossola, and Scaillet (2019, Journal of Econometrics 212: 503-521)的方法。共同因素通常是不可观察或不可观察的。在时间序列中，它们影响所有预测因子，而在面板数据模型中，它们在不同程度上影响所有截面单位。例如来自油价、通货膨胀或需求或供给冲击的冲击。关于因素数量的知识是多种计量经济学估计方法的关键，如Pesaran (2006, Econometrica 74: 967-1012)， Bai (2009, Econometrica 77: 1229-1279)， Norkute等人(2021,Journal of Econometrica 220: 416-446)， Kripfganz和Sarafidis (2021, Stata Journal 21: 659-686)。本文讨论了共10种估算公因子数量的方法。基于Kapetanios, Pesaran和Reese (2021, Journal of Econometrics 221: 510-541)的例子表明，美国房价受到多达10个共同因素的影响。因此，当一个人拟合以房价为因变量的模型时，必须考虑因素的数量。
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Pub Date : 2023-06-01DOI: 10.1177/1536867X231175334
Yuan Xue, Chuntao Li, Haitao Si
Reporting empirical results to automatically generate structured tables is important but time consuming for empirical researchers. Because of the lack of commands that can effectively create and edit Office Open XML documents (.docx documents), neither official commands nor community-contributed commands could tabulate results to this regularly used document type until putdocx was launched in Stata 15. In this article, we introduce four new commands: sum2docx, corr2docx, t2docx, and reg2docx. These new commands are all based on putdocx. They can be coalesced and can report summary statistics, correlation coefficient matrices, split-sample t tests, and regression results automatically in one .docx file. The commands are user friendly and can provide researchers with new options for reporting empirical results.
报告实证结果，以自动生成结构化表是重要的，但耗时的实证研究人员。由于缺乏能够有效地创建和编辑Office Open XML文档(.docx文档)的命令，官方命令和社区贡献的命令都无法将结果制表为这种经常使用的文档类型，直到Stata 15中推出putdocx。在本文中，我们将介绍四个新命令:sum2docx、corr2docx、t2docx和reg2docx。这些新命令都是基于putdocx的。它们可以合并在一起，并且可以在一个.docx文件中自动报告汇总统计信息、相关系数矩阵、分离样本t检验和回归结果。这些命令是用户友好的，可以为研究人员提供报告经验结果的新选项。
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Pub Date : 2023-06-01DOI: 10.1177/1536867X231175332
M. Overgaard, Per K. Andersen, E. Parner
Regression analyses of how state occupation probabilities or expected lengths of stay depend on covariates in multistate settings can be performed using the pseudo-observation method, which involves calculating jackknife pseudo-observations based on some estimator of the expected value of the outcome. In this article, we present a new command, stpmstate, that calculates such pseudo-observations based on the Aalen–Johansen estimator. We give examples of use of the command, and we conduct a small simulation study to offer insights into the pseudo-observation regression approach.
{"title":"Pseudo-observations in a multistate setting","authors":"M. Overgaard, Per K. Andersen, E. Parner","doi":"10.1177/1536867X231175332","DOIUrl":"https://doi.org/10.1177/1536867X231175332","url":null,"abstract":"Regression analyses of how state occupation probabilities or expected lengths of stay depend on covariates in multistate settings can be performed using the pseudo-observation method, which involves calculating jackknife pseudo-observations based on some estimator of the expected value of the outcome. In this article, we present a new command, stpmstate, that calculates such pseudo-observations based on the Aalen–Johansen estimator. We give examples of use of the command, and we conduct a small simulation study to offer insights into the pseudo-observation regression approach.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":null,"pages":null},"PeriodicalIF":4.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46167390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1177/1536867X231161977
Yingyao Hu, Guofang Huang, Yuya Sasaki
We introduce a new command, robustpf, to estimate parameters of Cobb–Douglas production functions. The command is robust against two potential problems. First, it is robust against optimization errors in firms’ input choice, unobserved idiosyncratic cost shocks, and measurement errors in proxy variables. In particular, the command relaxes the conventional assumption of scalar unobservables. Second, it is also robust against the functional dependence problem of static input choice, which is known today as a cause of identification failure. The main method is proposed by Hu, Huang, and Sasaki (2020, Journal of Econometrics 215: 375–398).
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