计量经济学的新手在这里陷入了困境。感谢您提出的所有建议，无法找到答案！
我的问题是，当同一年在同一行业中有多家公司时，如何在plm中包括行业和年度固定影响？ 我的数据的中继看起来像这样：
Year Industry CompanyID CEOID CEO.background MBA.CEO CEO.Tenure Female.CEO CEO.age Capex Log.TA Leverage
2005 6 1075 10739 0 0 6.92 0 55 0.08623238 9.199961396 0.330732917
2006 6 1075 10739 0 0 7.92 0 56 0.097455145 9.334559982 0.26575725
2007 6 1075 10739 0 0 8.92 0 56 0.113033772 9.346263914 0.285439531
2008 6 1075 10739 0 0 9.92 0 57 0.108640177 9.327564318 0.322985772
2009 6 1075 5835 0 0 0.67 0 54 0.08526524 9.360491034 0.333880116
2010 6 1075 5835 0 0 1.67 0 55 0.081452292 9.376545673 0.32197511
2005 6 1743 8379 0 0 17.43 0 65 0.236487293 6.693007633 0.021915227
2006 6 1743 26012 0 1 0.91 0 59 0.319264835 6.820455133 0.023157959
2007 6 1743 26012 0 1 1.91 0 58 0.207384938 6.844512984 0.020087012
2008 6 1743 26012 0 1 2.92 0 59 0.130632264 6.890964093 0.017103795
2009 6 1743 26012 0 1 3.92 0 60 0.112029325 6.879662342 0.017283796
2010 6 1743 30801 0 0 1 1 47 0.02804693 6.767971236 0.044755539
2005 7 1004 9249 0 0 9.65 0 53 0.076370794 6.596094672 0.31534354
2006 7 1004 9249 0 0 10.65 0 54 0.114891589 6.886346743 0.327808308
2007 7 1004 9249 0 0 11.65 0 55 0.097727719 6.973199328 0.307086799
2008 7 1004 9249 0 0 12.65 0 56 0.112119583 7.216716829 0.389800369
2009 7 1004 9249 0 0 13.65 0 57 0.086281135 7.228033526 0.331455792
2010 7 1004 9249 0 0 14.65 0 58 0.298922358 7.313914813 0.291147083
CEO.background，MBA.CEO和Female.ceo是每个CEO的时变假人和公司的行业时变假人，而其余是时变的公司/ ceo属性。
我想对行业/年回归代码运行以下固定影响：
plm(Capex ~ CEO.background + MBA.CEO + CEO.Tenure + Female.CEO + CEO.age + Log.TA + Leverage, data=repexcapex, index = (c("Industry", "Year")), model = "within", effect = "twoways")
但是，如果我在同一行业中有多家公司，例如上述数据（行业6中的公司ID均为1075/1743，则代码会给出有关重复项的错误。
Error in pdim.default(index[[1]], index[[2]]) :
duplicate couples (id-time)
In addition: Warning messages:
1: In pdata.frame(data, index) :
duplicate couples (id-time) in resulting pdata.frame
[...]
[如果我杀死前5行，并且每个行业仅由1家公司经营，则代码有效。
我应该如何制定回归模型以包含行业和年度固定影响？正在使用下面的行业虚拟变量运行代码，等同于行业固定效果：
plm(Capex ~ CEO.background + MBA.CEO + CEO.Tenure + Female.CEO + CEO.age + Log.TA + Leverage + factor(Industries), data=repexcapex, index = (c("Year")), model = "within", effect = "individual")
这是格式化的数据：
repexcapex <- read.table(text="
Year,Industry,CompanyID,CEOID,CEO.background,MBA.CEO,CEO.Tenure,Female.CEO,CEO.age,Capex,Log.TA,Leverage
2005,6,1075,10739,0,0,6.92,0,55,0.08623238,9.199961396,0.330732917
2006,6,1075,10739,0,0,7.92,0,56,0.097455145,9.334559982,0.26575725
2007,6,1075,10739,0,0,8.92,0,56,0.113033772,9.346263914,0.285439531
2008,6,1075,10739,0,0,9.92,0,57,0.108640177,9.327564318,0.322985772
2009,6,1075,5835,0,0,0.67,0,54,0.08526524,9.360491034,0.333880116
2010,6,1075,5835,0,0,1.67,0,55,0.081452292,9.376545673,0.32197511
2005,6,1743,8379,0,0,17.43,0,65,0.236487293,6.693007633,0.021915227
2006,6,1743,26012,0,1,0.91,0,59,0.319264835,6.820455133,0.023157959
2007,6,1743,26012,0,1,1.91,0,58,0.207384938,6.844512984,0.020087012
2008,6,1743,26012,0,1,2.92,0,59,0.130632264,6.890964093,0.017103795
2009,6,1743,26012,0,1,3.92,0,60,0.112029325,6.879662342,0.017283796
2010,6,1743,30801,0,0,1,1,47,0.02804693,6.767971236,0.044755539
2005,7,1004,9249,0,0,9.65,0,53,0.076370794,6.596094672,0.31534354
2006,7,1004,9249,0,0,10.65,0,54,0.114891589,6.886346743,0.327808308
2007,7,1004,9249,0,0,11.65,0,55,0.097727719,6.973199328,0.307086799
2008,7,1004,9249,0,0,12.65,0,56,0.112119583,7.216716829,0.389800369
2009,7,1004,9249,0,0,13.65,0,57,0.086281135,7.228033526,0.331455792
2010,7,1004,9249,0,0,14.65,0,58,0.298922358,7.313914813,0.291147083",
sep=",",header=TRUE)
由于您的因变量Capex似乎是公司特定的度量，因此观察单位（= plm称为个体维度的单位）很可能是公司（变量CompanyID），将在[ C0]参数。
这可以通过以下方式估算基本的2通模型：
index要添加行业固定效果，请在公式中包括summary(plm(Capex ~ CEO.background + MBA.CEO + CEO.Tenure + Female.CEO + CEO.age + Log.TA + Leverage, data=repexcapex, index = (c("CompanyID", "Year")), model = "within", effect = "twoways"))
。该变量可能会与其他固定效应相关联（这是针对您提供的小样本数据），因此可能会脱离估算值。