Stata面板设置与面板数据多元线性回归与泊松回归命令

大家好，我是讯享网，很高兴认识大家。

1. 设置面板

[XT] xtset – Declare data to be panel data

xtset panelvar timevar [, tsoptions]

讯享网

讯享网# example xtset business_id year

常用面板数据命令 (具体用法用help查一下)

xtset: Declare a dataset to be panel data xtdescribe: Describe pattern of xt data xtsum: Summarize xt data xttab: Tabulate xt data xtdata: Faster specification searches with xt data xtline: Line plots with xt data xtreg: Fixed-, between- and random-effects, and population-averaged linear models xtregar: Fixed- and random-effects linear models with an AR(1) disturbance xtmixed: Multilevel mixed-effects linear regression xtgls: Panel-data models using GLS xtpcse: OLS or Prais-Winsten models with panel-corrected standard errors xthtaylor; Hausman-Taylor estimator for error-components models xtfrontier: Stochastic frontier models for panel data xtrc: Random coefficients models xtivreg: Instrumental variables and two-stage least squares for panel-data models xtunitroot: Panel-data unit-root tests xtabond: Arellano-Bond linear dynamic panel-data estimator xtdpdsys Arellano-Bond/Blundell-Bond estimation xtdpd: Linear dynamic panel-data estimation xttobit: Random-effects tobit models xtintreg: Random-effects interval-data regression models xtlogit: Fixed-effects, random-effects, & population-averaged logit models xtprobit: Random-effects and population-averaged probit models xtcloglog: Random-effects and population-averaged cloglog models xtpoisson: Fixed-effects, random-effects, & population-averaged Poisson models xtnbreg: Fixed-effects, random-effects, & population-averaged negative binomial models xtmelogit: Multilevel mixed-effects logistic regression xtmepoisson: Multilevel mixed-effects Poisson regression xtgee: Population-averaged panel-data models using GEE

参考来源：Stata：面板数据计量方法汇总

面板设置常见问题

1.1 pannel variable为string类型

把 string 转换为数字类型

讯享网# 自己改一下pannel variable encode business_id, gen(business_id_1) drop business_id rename business_id_1 business_id

1.2 时间变量为string类型

如果是只有年份的数据，可以直接把字符串转换为数值类型

destring year, replace force

如果时间变量还包含日期，则需要多一步转换

我的日期数据是这样的，包括了时分秒
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讯享网

讯享网# 把时间转换成双精度数值类型 gen double time = clock(date, "YMDhms")

转换后就会成为一串数值
在这里插入图片描述

stata常用时间命令，根据自己时间数据的格式以及需求选择合适的来用

Clock(s1,s2[,Y]) clock(s1,s2[,Y]) date(s1,s2[,Y]) daily(s1,s2[,Y])

具体还可以参考：
STATA日期函数汇总
【STATA】时间变量处理，字符串转为日期时间

1.3 报错：repeated time values within panel

面板数据下同一vari不允许出现同样的时间，因此我们需要把重复的数据去除

讯享网# 报告重复数据 duplicates report business_id time # 列出重复数据 duplicates list business_id time # 去除重复数据 duplicates drop business_id time, force

2. 面板数据多元线性回归

[XT] xtreg – Fixed-, between-, and random-effects and population-averaged linear models

GLS random-effects (RE) model xtreg depvar [indepvars] [if] [in] [, re RE_options] Between-effects (BE) model xtreg depvar [indepvars] [if] [in] , be [BE_options] Fixed-effects (FE) model xtreg depvar [indepvars] [if] [in] [weight] , fe [FE_options] ML random-effects (MLE) model xtreg depvar [indepvars] [if] [in] [weight] , mle [MLE_options] Population-averaged (PA) model xtreg depvar [indepvars] [if] [in] [weight] , pa [PA_options]

讯享网# 一维聚类调整异方差-序列相关稳健型标准误 (HAC) xtreg like stars text_len sentiment polarity i.year, fe vce(cluster business_id)

在这里插入图片描述

# 二维聚类 SE webuse "nlswork.dta", clear vce2way regress ln_wage age grade, cluster(idcode year)

参考来源：Stata 连享会: 聚类调整标准误笔记

3. 面板数据泊松回归

[XT] xtpoisson – Fixed-effects, random-effects, and population-averaged Poisson models

讯享网Random-effects (RE) model xtpoisson depvar [indepvars] [if] [in] [weight] [, re RE_options] Conditional fixed-effects (FE) model xtpoisson depvar [indepvars] [if] [in] [weight] , fe [FE_options] Population-averaged (PA) model xtpoisson depvar [indepvars] [if] [in] [weight] , pa [PA_options]

example

# 一维聚类调整异方差-序列相关稳健型标准误 (HAC) xtpoisson like stars text_len sentiment polarity i.year, vce(robust) i(business_id) fe