Pub Date : 2022-06-01DOI: 10.1177/1536867X221106371
Alifyah Y Kagalwala
Commonly used unit-root tests in time-series analysis—such as the Dickey–Fuller and Phillips–Perron tests—use a null hypothesis that the series contains a unit root. Such tests have low power against the alternative—when a time series is near integrated or highly autoregressive—implying that they do poorly in distinguishing such a series from having a unit root. Kwiatkowski et al. (1992, Journal of Econometrics 54: 159–178) introduced the Kwiatkowski, Phillips, Schmidt, and Shin test, in which the null hypothesis is that the series is stationary, to deal with this problem. One shortcoming of the presently available Kwiatkowski, Phillips, Schmidt, and Shin test in Stata is that it uses asymptotic critical values regardless of the sample size. This poses a problem in that researchers—especially social scientists—are often presented with short time series. I introduce kpsstest, a command that extends the previous implementation by including an option for a zero-mean-stationary null hypothesis, generating sample and test-specific critical values, and reporting appropriate p-values.
时间序列分析中常用的单位根检验，如Dickey–Fuller和Phillips–Perron检验，使用序列包含单位根的零假设。当时间序列接近积分或高度自回归时，这种测试相对于另一种测试的功率较低，这意味着它们在区分这种序列与具有单位根方面做得很差。Kwiatkowski等人（1992，Journal of Econometrics 54:159–178）引入了Kwiatkovski、Phillips、Schmidt和Shin检验，其中零假设是序列是平稳的，以处理这个问题。Stata中目前可用的Kwiatkowski、Phillips、Schmidt和Shin检验的一个缺点是，无论样本大小，它都使用渐近临界值。这带来了一个问题，因为研究人员——尤其是社会科学家——经常被呈现出短时间序列。我介绍了kpsstest，这是一个命令，它扩展了以前的实现，包括零均值平稳零假设的选项，生成样本和测试特定的临界值，并报告适当的p值。
{"title":"kpsstest: A command that implements the Kwiatkowski, Phillips, Schmidt, and Shin test with sample-specific critical values and reports p-values","authors":"Alifyah Y Kagalwala","doi":"10.1177/1536867X221106371","DOIUrl":"https://doi.org/10.1177/1536867X221106371","url":null,"abstract":"Commonly used unit-root tests in time-series analysis—such as the Dickey–Fuller and Phillips–Perron tests—use a null hypothesis that the series contains a unit root. Such tests have low power against the alternative—when a time series is near integrated or highly autoregressive—implying that they do poorly in distinguishing such a series from having a unit root. Kwiatkowski et al. (1992, Journal of Econometrics 54: 159–178) introduced the Kwiatkowski, Phillips, Schmidt, and Shin test, in which the null hypothesis is that the series is stationary, to deal with this problem. One shortcoming of the presently available Kwiatkowski, Phillips, Schmidt, and Shin test in Stata is that it uses asymptotic critical values regardless of the sample size. This poses a problem in that researchers—especially social scientists—are often presented with short time series. I introduce kpsstest, a command that extends the previous implementation by including an option for a zero-mean-stationary null hypothesis, generating sample and test-specific critical values, and reporting appropriate p-values.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"22 1","pages":"269 - 292"},"PeriodicalIF":4.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43112305","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 : 2022-06-01DOI: 10.1177/1536867X221106404
David M. Kaplan
In this article, I introduce the sivqr command, which estimates the coefficients of the instrumental variables quantile regression model introduced by Chernozhukov and Hansen (2005, Econometrica 73: 245–261). The sivqr command offers several advantages over the existing ivqreg and ivqreg2 commands for estimating this instrumental variables quantile regression model, which complements the alternative “triangular model” behind cqiv and the “local quantile treatment effect” model of ivqte. Computationally, sivqr implements the smoothed estimator of Kaplan and Sun (2017, Econometric Theory 33: 105–157), who show that smoothing improves both computation time and statistical accuracy. Standard errors are computed analytically or by Bayesian bootstrap; for nonindependent and identically distributed sampling, sivqr is compatible with bootstrap. I discuss syntax and the underlying methodology, and I compare sivqr with other commands in an example.
{"title":"Smoothed instrumental variables quantile regression","authors":"David M. Kaplan","doi":"10.1177/1536867X221106404","DOIUrl":"https://doi.org/10.1177/1536867X221106404","url":null,"abstract":"In this article, I introduce the sivqr command, which estimates the coefficients of the instrumental variables quantile regression model introduced by Chernozhukov and Hansen (2005, Econometrica 73: 245–261). The sivqr command offers several advantages over the existing ivqreg and ivqreg2 commands for estimating this instrumental variables quantile regression model, which complements the alternative “triangular model” behind cqiv and the “local quantile treatment effect” model of ivqte. Computationally, sivqr implements the smoothed estimator of Kaplan and Sun (2017, Econometric Theory 33: 105–157), who show that smoothing improves both computation time and statistical accuracy. Standard errors are computed analytically or by Bayesian bootstrap; for nonindependent and identically distributed sampling, sivqr is compatible with bootstrap. I discuss syntax and the underlying methodology, and I compare sivqr with other commands in an example.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"22 1","pages":"379 - 403"},"PeriodicalIF":4.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43501561","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 : 2022-06-01DOI: 10.1177/1536867X221106403
Christopher F. Baum, S. Hurn, Jesús Otero
The concept of Granger causality is an important tool in applied macroeconomics. Recently, recursive econometric methods have been developed to analyze the temporal stability of Granger-causal relationships. This article offers an implementation of these recursive procedures in Stata. An empirical example illustrates their use in analyzing the temporal stability of Granger causality among key U.S. macroeconomic series.
{"title":"Testing for time-varying Granger causality","authors":"Christopher F. Baum, S. Hurn, Jesús Otero","doi":"10.1177/1536867X221106403","DOIUrl":"https://doi.org/10.1177/1536867X221106403","url":null,"abstract":"The concept of Granger causality is an important tool in applied macroeconomics. Recently, recursive econometric methods have been developed to analyze the temporal stability of Granger-causal relationships. This article offers an implementation of these recursive procedures in Stata. An empirical example illustrates their use in analyzing the temporal stability of Granger causality among key U.S. macroeconomic series.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"22 1","pages":"355 - 378"},"PeriodicalIF":4.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42172050","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 : 2022-06-01DOI: 10.1177/1536867X221106402
Yuya Sasaki, T. Ura
We introduce a new command, robustate, that executes the inverseprobability weighting estimation and inference for the average treatment effect with robustness against limited overlap (that is, weak satisfaction of the common support condition). This command produces estimates, standard errors, p-values, and confidence intervals for the average treatment effect. The utility of the command is demonstrated with both simulated and real data of right heart catheterization. These illustrations show that the proposed estimator implemented by the robustate command indeed exhibits more robustness against limited overlap than the traditional inverse-probability weighting estimator. The main method of the command is proposed in Sasaki and Ura (2022, Econometric Theory 38: 66–112).
{"title":"Average treatment effect estimates robust to the “limited overlap” problem: robustate","authors":"Yuya Sasaki, T. Ura","doi":"10.1177/1536867X221106402","DOIUrl":"https://doi.org/10.1177/1536867X221106402","url":null,"abstract":"We introduce a new command, robustate, that executes the inverseprobability weighting estimation and inference for the average treatment effect with robustness against limited overlap (that is, weak satisfaction of the common support condition). This command produces estimates, standard errors, p-values, and confidence intervals for the average treatment effect. The utility of the command is demonstrated with both simulated and real data of right heart catheterization. These illustrations show that the proposed estimator implemented by the robustate command indeed exhibits more robustness against limited overlap than the traditional inverse-probability weighting estimator. The main method of the command is proposed in Sasaki and Ura (2022, Econometric Theory 38: 66–112).","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"22 1","pages":"344 - 354"},"PeriodicalIF":4.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42129493","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 : 2022-06-01DOI: 10.1177/1536867X221106417
N. Bruun
In statistical work, it is essential to have an overview of the data used. In, for example, biomedical articles, a standardized way of reporting summaries of continuous and categorical variables is “table 1”. This standardized way of reporting can be useful in most cases of statistical work. The basetable command is a flexible and straightforward way to build and format such table reports. The final reports are easy to style into Stata Markup and Control Language, commaseparated values, HyperText Markup Language, LATEX or TEX, or Markdown and, for example, save into a file specified by the using modifier. Also, it is possible to export the reports created by basetable into Excel worksheets. Because of the General Data Protection Regulation, it has become necessary to blur information on individuals when making reports; in basetable, there are options to blur both categorical and continuous data.
{"title":"Interactively building table reports with basetable","authors":"N. Bruun","doi":"10.1177/1536867X221106417","DOIUrl":"https://doi.org/10.1177/1536867X221106417","url":null,"abstract":"In statistical work, it is essential to have an overview of the data used. In, for example, biomedical articles, a standardized way of reporting summaries of continuous and categorical variables is “table 1”. This standardized way of reporting can be useful in most cases of statistical work. The basetable command is a flexible and straightforward way to build and format such table reports. The final reports are easy to style into Stata Markup and Control Language, commaseparated values, HyperText Markup Language, LATEX or TEX, or Markdown and, for example, save into a file specified by the using modifier. Also, it is possible to export the reports created by basetable into Excel worksheets. Because of the General Data Protection Regulation, it has become necessary to blur information on individuals when making reports; in basetable, there are options to blur both categorical and continuous data.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"22 1","pages":"416 - 429"},"PeriodicalIF":4.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44163080","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 : 2022-06-01DOI: 10.1177/1536867X221106416
Patricio Troncoso, Ana Morales-Gómez
In randomized controlled trials, intention-to-treat analysis is customarily used to estimate the effect of the trial. However, in the presence of noncompliance, this can often lead to biased estimates because intention-to-treat analysis completely ignores varying levels of actual treatment received. This is a known issue that can be overcome by adopting the complier average causal effect approach, which estimates the effect the trial had on the individuals who complied with the protocol. When compliance is unobserved in the control group, the complier average causal effect estimate can be obtained via a latent class specification using the gsem command.
{"title":"Estimating the complier average causal effect via a latent class approach using gsem","authors":"Patricio Troncoso, Ana Morales-Gómez","doi":"10.1177/1536867X221106416","DOIUrl":"https://doi.org/10.1177/1536867X221106416","url":null,"abstract":"In randomized controlled trials, intention-to-treat analysis is customarily used to estimate the effect of the trial. However, in the presence of noncompliance, this can often lead to biased estimates because intention-to-treat analysis completely ignores varying levels of actual treatment received. This is a known issue that can be overcome by adopting the complier average causal effect approach, which estimates the effect the trial had on the individuals who complied with the protocol. When compliance is unobserved in the control group, the complier average causal effect estimate can be obtained via a latent class specification using the gsem command.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"22 1","pages":"404 - 415"},"PeriodicalIF":4.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47745915","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 : 2022-06-01DOI: 10.1177/1536867X221106437
M. Falcaro, R. Newson, P. Sasieni
After fitting a Poisson regression model to evaluate the effect of an intervention in a cohort study, one might be interested in estimating the number of events prevented by the intervention (assuming the observed associations are causal). This can be derived as the difference in the intervention group between the predicted number of events under the counterfactual (no intervention) and the factual (intervention) scenarios. One could use the predict command to obtain the predicted number of events under the two scenarios and then sum up the differences, but this approach would not be conve-nient for several reasons. One would need to change the intervention variable to get the counterfactual predicted values, and the confidence intervals would not be readily available ( bootstrap or jackknife could be used, but this could be particularly time consuming if the dataset is large). We here suggest the margins command. Its use, however, is not straight-forward for our specific problem margins computes observation then the these
{"title":"Stata tip 146: Using margins after a Poisson regression model to estimate the number of events prevented by an intervention","authors":"M. Falcaro, R. Newson, P. Sasieni","doi":"10.1177/1536867X221106437","DOIUrl":"https://doi.org/10.1177/1536867X221106437","url":null,"abstract":"After fitting a Poisson regression model to evaluate the effect of an intervention in a cohort study, one might be interested in estimating the number of events prevented by the intervention (assuming the observed associations are causal). This can be derived as the difference in the intervention group between the predicted number of events under the counterfactual (no intervention) and the factual (intervention) scenarios. One could use the predict command to obtain the predicted number of events under the two scenarios and then sum up the differences, but this approach would not be conve-nient for several reasons. One would need to change the intervention variable to get the counterfactual predicted values, and the confidence intervals would not be readily available ( bootstrap or jackknife could be used, but this could be particularly time consuming if the dataset is large). We here suggest the margins command. Its use, however, is not straight-forward for our specific problem margins computes observation then the these","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"22 1","pages":"460 - 464"},"PeriodicalIF":4.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46715682","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 : 2022-06-01DOI: 10.1177/1536867X221106436
N. Cox
How do you work with the largest five, or smallest five, or any other fixed number of values in a tail of a distribution? In this column, I give examples of problems and code for basic calculations as a prelude to graphics, tables, and more detailed analysis. The main illustration is analysis of concentration among firms or companies, with wider discussion mentioning hydrology, climatology, cryptography, and ecology. The examples allow a tutorial covering sorting and ranking and using if and in to select observations, by: as a framework for groupwise calculations, indicator variables as a mode of selection, and egen as a Swiss Army knife with many handy functions.
{"title":"Speaking Stata: The largest five—A tale of tail values","authors":"N. Cox","doi":"10.1177/1536867X221106436","DOIUrl":"https://doi.org/10.1177/1536867X221106436","url":null,"abstract":"How do you work with the largest five, or smallest five, or any other fixed number of values in a tail of a distribution? In this column, I give examples of problems and code for basic calculations as a prelude to graphics, tables, and more detailed analysis. The main illustration is analysis of concentration among firms or companies, with wider discussion mentioning hydrology, climatology, cryptography, and ecology. The examples allow a tutorial covering sorting and ranking and using if and in to select observations, by: as a framework for groupwise calculations, indicator variables as a mode of selection, and egen as a Swiss Army knife with many handy functions.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"22 1","pages":"446 - 459"},"PeriodicalIF":4.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45542509","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 : 2022-06-01DOI: 10.1177/1536867x221106438
N. Cox
{"title":"Erratum: Stata tip 145: Numbering weeks within months","authors":"N. Cox","doi":"10.1177/1536867x221106438","DOIUrl":"https://doi.org/10.1177/1536867x221106438","url":null,"abstract":"","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"22 1","pages":"465 - 466"},"PeriodicalIF":4.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45999549","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}
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