Pub Date : 2023-03-01DOI: 10.1177/1536867X231162031
Fabrizio Colella, R. Lalive, S. Sakalli, Mathias Thoenig
We present acreg, a new command that implements the arbitrary clustering correction of standard errors proposed in Colella et al. (2019, IZA discussion paper 12584). Arbitrary here refers to the way observational units are correlated with each other: we impose no restrictions so that our approach can be used with a wide range of data. The command accommodates both cross-sectional and panel databases and allows the estimation of ordinary least-squares and two-stage least-squares coefficients, correcting standard errors in three environments: in a spatial setting using units’ coordinates or distance between units, in a network setting starting from the adjacency matrix, and in a multiway clustering framework taking multiple clustering variables as input. Distance and time cutoffs can be specified by the user, and linear decays in time and space are also optional.
{"title":"acreg: Arbitrary correlation regression","authors":"Fabrizio Colella, R. Lalive, S. Sakalli, Mathias Thoenig","doi":"10.1177/1536867X231162031","DOIUrl":"https://doi.org/10.1177/1536867X231162031","url":null,"abstract":"We present acreg, a new command that implements the arbitrary clustering correction of standard errors proposed in Colella et al. (2019, IZA discussion paper 12584). Arbitrary here refers to the way observational units are correlated with each other: we impose no restrictions so that our approach can be used with a wide range of data. The command accommodates both cross-sectional and panel databases and allows the estimation of ordinary least-squares and two-stage least-squares coefficients, correcting standard errors in three environments: in a spatial setting using units’ coordinates or distance between units, in a network setting starting from the adjacency matrix, and in a multiway clustering framework taking multiple clustering variables as input. Distance and time cutoffs can be specified by the user, and linear decays in time and space are also optional.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"23 1","pages":"119 - 147"},"PeriodicalIF":4.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45943556","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: 10.1177/1536867X231162033
Yujun Lian, Chang Liu, Christopher F. Parmeter
In this article, we introduce the sftt command, which fits two-tier stochastic frontier (2TSF) models with cross-sectional data. Like most frontier models, a 2TSF model consists of a linear frontier model and a composite error term. The error term is assumed to be a mixture of three components: two onesided inefficiency terms—strictly nonnegative and nonpositive, respectively—and a symmetric noise term. When providing appropriate distributional assumptions, sftt can fit models with exponential and half-normal specifications. sftt also fits 2TSF models with the scaling property to mitigate concerns over distributional specifications. In addition, we provide two subcommands, sftt sigs and sftt eff, to assist in postestimation efficiency analysis. We provide an overview of the 2TSF literature, a description of the sftt command and its options, and examples using simulated and actual data.
{"title":"Two-tier stochastic frontier analysis using Stata","authors":"Yujun Lian, Chang Liu, Christopher F. Parmeter","doi":"10.1177/1536867X231162033","DOIUrl":"https://doi.org/10.1177/1536867X231162033","url":null,"abstract":"In this article, we introduce the sftt command, which fits two-tier stochastic frontier (2TSF) models with cross-sectional data. Like most frontier models, a 2TSF model consists of a linear frontier model and a composite error term. The error term is assumed to be a mixture of three components: two onesided inefficiency terms—strictly nonnegative and nonpositive, respectively—and a symmetric noise term. When providing appropriate distributional assumptions, sftt can fit models with exponential and half-normal specifications. sftt also fits 2TSF models with the scaling property to mitigate concerns over distributional specifications. In addition, we provide two subcommands, sftt sigs and sftt eff, to assist in postestimation efficiency analysis. We provide an overview of the 2TSF literature, a description of the sftt command and its options, and examples using simulated and actual data.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"23 1","pages":"197 - 229"},"PeriodicalIF":4.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46051419","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: 10.1177/1536867X231160122
N. Cox, S. Jenkins
{"title":"Announcement of the Stata Journal Editors’ Prize 2023","authors":"N. Cox, S. Jenkins","doi":"10.1177/1536867X231160122","DOIUrl":"https://doi.org/10.1177/1536867X231160122","url":null,"abstract":"","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"23 1","pages":"1 - 2"},"PeriodicalIF":4.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48853908","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: 10.1177/1536867X231162020
Carlo Lazzaro
{"title":"Stata tip 150: When is it appropriate to xtset a panel dataset with panelvar only?","authors":"Carlo Lazzaro","doi":"10.1177/1536867X231162020","DOIUrl":"https://doi.org/10.1177/1536867X231162020","url":null,"abstract":"","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"23 1","pages":"281 - 292"},"PeriodicalIF":4.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49174408","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: 10.1177/1536867x231162009
N. Cox
{"title":"Stata tip 151: Puzzling out some logical operators","authors":"N. Cox","doi":"10.1177/1536867x231162009","DOIUrl":"https://doi.org/10.1177/1536867x231162009","url":null,"abstract":"","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"23 1","pages":"293 - 297"},"PeriodicalIF":4.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43506111","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 : 2022-12-01DOI: 10.1177/1536867X221141021
Hongbing Zhu, Lihua Yang
Portfolio analysis is widely used in empirical asset pricing to explore the cross-sectional relation between two or more variables. In this article, we introduce the methodology of portfolio analysis and describe a new command, portfolio, that provides a one-step solution for portfolio analysis. portfolio calculates the equal- or value-weighted returns with a t statistic for the portfolio and tests the significance of a long-short strategy in portfolios. portfolio also provides the Newey–West standard-error adjustment option for alleviating the impact of potential autocorrelation and heteroskedasticity in financial time series.
{"title":"portfolio: A command for conducting portfolio analysis in Stata","authors":"Hongbing Zhu, Lihua Yang","doi":"10.1177/1536867X221141021","DOIUrl":"https://doi.org/10.1177/1536867X221141021","url":null,"abstract":"Portfolio analysis is widely used in empirical asset pricing to explore the cross-sectional relation between two or more variables. In this article, we introduce the methodology of portfolio analysis and describe a new command, portfolio, that provides a one-step solution for portfolio analysis. portfolio calculates the equal- or value-weighted returns with a t statistic for the portfolio and tests the significance of a long-short strategy in portfolios. portfolio also provides the Newey–West standard-error adjustment option for alleviating the impact of potential autocorrelation and heteroskedasticity in financial time series.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"22 1","pages":"941 - 957"},"PeriodicalIF":4.8,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43083102","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 : 2022-12-01DOI: 10.1177/1536867X221141057
M. Bottai
Incidence rates are popular summary measures of the occurrence over time of events of interest. They are also called mortality rates or failure rates, depending on the context. The incidence rate is defined as the ratio between the total number of events and total follow-up time and can be estimated with the strate command. When the event of interest can occur multiple times on any given subject over a time period, like infections, the incidence rate represents an average count per unit of time, such as the average number of infections per year. When the event of interest can occur only once, such as death, an alternative summary measure is the risk, or probability, of occurrence per unit time, such as the risk of dying in one year. In this article, I present the stprisk command, which estimates risks, and illustrate its use and interpretation through a data example.
{"title":"Estimating the risk of events with stprisk","authors":"M. Bottai","doi":"10.1177/1536867X221141057","DOIUrl":"https://doi.org/10.1177/1536867X221141057","url":null,"abstract":"Incidence rates are popular summary measures of the occurrence over time of events of interest. They are also called mortality rates or failure rates, depending on the context. The incidence rate is defined as the ratio between the total number of events and total follow-up time and can be estimated with the strate command. When the event of interest can occur multiple times on any given subject over a time period, like infections, the incidence rate represents an average count per unit of time, such as the average number of infections per year. When the event of interest can occur only once, such as death, an alternative summary measure is the risk, or probability, of occurrence per unit time, such as the risk of dying in one year. In this article, I present the stprisk command, which estimates risks, and illustrate its use and interpretation through a data example.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"22 1","pages":"969 - 974"},"PeriodicalIF":4.8,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44114491","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 : 2022-12-01DOI: 10.1177/1536867X221141067
R. Newson
Stata, like R and Genstat, is a statistical language with potentially as many dialects as users. These dialects are defined by the optional packages that each user has downloaded; the number may be hundreds for a high-power user. This is a major advantage of a statistical language (such as Stata) over a statistical package (such as SPSS). And Stata users would like to continue to have that advantage on web-disabled machines.
{"title":"Stata tip 147: Porting downloaded packages between machines","authors":"R. Newson","doi":"10.1177/1536867X221141067","DOIUrl":"https://doi.org/10.1177/1536867X221141067","url":null,"abstract":"Stata, like R and Genstat, is a statistical language with potentially as many dialects as users. These dialects are defined by the optional packages that each user has downloaded; the number may be hundreds for a high-power user. This is a major advantage of a statistical language (such as Stata) over a statistical package (such as SPSS). And Stata users would like to continue to have that advantage on web-disabled machines.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"22 1","pages":"996 - 997"},"PeriodicalIF":4.8,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48434266","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 : 2022-12-01DOI: 10.1177/1536867X221140943
Takuya Hasebe
In this article, I describe the commands that implement the estimation of three endogenous models of binary choice outcome. The command esbinary fits the endogenously switching model, where a potential outcome differs across two treatment states. The command edbinary fits the endogenous dummy model, which includes a dummy variable indicating the treatment state as one of the explanatory variables. After one estimates the parameters of these models, various treatment effects can be estimated as postestimation statistics. The command ssbinary fits the sample-selection model, where an outcome is observed in only one of the states. The commands fit these models using copula-based maximumlikelihood estimation.
{"title":"Endogenous models of binary choice outcomes: Copula-based maximum-likelihood estimation and treatment effects","authors":"Takuya Hasebe","doi":"10.1177/1536867X221140943","DOIUrl":"https://doi.org/10.1177/1536867X221140943","url":null,"abstract":"In this article, I describe the commands that implement the estimation of three endogenous models of binary choice outcome. The command esbinary fits the endogenously switching model, where a potential outcome differs across two treatment states. The command edbinary fits the endogenous dummy model, which includes a dummy variable indicating the treatment state as one of the explanatory variables. After one estimates the parameters of these models, various treatment effects can be estimated as postestimation statistics. The command ssbinary fits the sample-selection model, where an outcome is observed in only one of the states. The commands fit these models using copula-based maximumlikelihood estimation.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"22 1","pages":"734 - 771"},"PeriodicalIF":4.8,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47252421","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：