来自Stata的xtpcse -如何在R中重写

Question

我目前正在学习R。我以前对STATA一无所知。

我想重新分析一项在Stata中完成的研究(xtpcse线性回归与面板校正的标准误差)。我在Stata中找不到模型或更详细的代码，也找不到如何在R中重写的任何其他提示。我已经为R安装了计量经济学的plm包，这就是我所得到的。

来自STATA的.do文件的第一行被复制到下面(我刚刚看到它非常不可读。下面是我在其中复制.do内容的txt文件的链接：http://dl.dropbox.com/u/4004629/This%20was%20in%20the%20.do%20file.txt)。我不知道如何以一种更好的方式来做这件事。我尝试了谷歌搜索、STATA和R比较等方法，但都不起作用。

我想要复制的研究的所有数据都在这里：

https://umdrive.memphis.edu/rblanton/public/ISQ_data

---STATA---
Group variable:   c_code                        Number of obs      =       265
Time variable:    year                          Number of groups   =        27
Panels:           correlated (unbalanced)       Obs per group: min =         3
Autocorrelation:  common AR(1)                                 avg =  9.814815
Sigma computed by pairwise selection                           max =        14
Estimated covariances      =       378          R-squared          =    0.8604
Estimated autocorrelations =         1          Wald chi2(11)      =   8321.15
Estimated coefficients     =        15          Prob > chi2        =    0.0000

------------------------------------------------------------------------------
             |           Panel-corrected
        food |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    lag_food |   .8449038    .062589    13.50   0.000     .7222316     .967576
        ciri |   -.010843   .0222419    -0.49   0.626    -.0544364    .0327504
   human_cap |   .0398406   .0142954     2.79   0.005     .0118222    .0678591
  worker_rts |  -.1132705   .0917999    -1.23   0.217    -.2931951     .066654
    polity_4 |   .0113995    .014002     0.81   0.416    -.0160439    .0388429
 market_size |   .0322474   .0696538     0.46   0.643    -.1042716    .1687665
      income |   .0382918   .0979499     0.39   0.696    -.1536865    .2302701
 econ_growth |   .0145589   .0105009     1.39   0.166    -.0060224    .0351402
   log_trade |  -.3062828   .1039597    -2.95   0.003    -.5100401   -.1025256
  fix_dollar |  -.0351874   .1129316    -0.31   0.755    -.2565293    .1861545
    fixed_xr |  -.4941214   .2059608    -2.40   0.016     -.897797   -.0904457
    xr_fluct |   .0019044   .0106668     0.18   0.858    -.0190021    .0228109
  lab_growth |   .0396278   .0277936     1.43   0.154    -.0148466    .0941022
     english |  -.1594438   .1963916    -0.81   0.417    -.5443641    .2254766
       _cons |   .4179213   1.656229     0.25   0.801    -2.828227     3.66407
-------------+----------------------------------------------------------------
         rho |   .0819359
------------------------------------------------------------------------------

. xtpcse fab_metal lag_fab_metal ciri human_cap worker_rts polity_4 market
> income econ_growth log_trade fix_dollar fixed_xr xr_fluct lab_growth
> english, pairwise corr(ar1)

更新：

我刚试过文森特的代码。我尝试了pcse2和vcovBK代码，它们都可以工作(尽管我不确定如何处理vcocBK产生的相关矩阵)。

然而，在我正在重新分析的论文中，我仍然无法重现回归系数的估计。我尽我所能地遵循他们的方法，我想，我唯一遗漏的一步是，在Stata中“自相关:公共AR(1)”的部分已经完成。我正在分析的论文说："OLS回归使用面板校正标准误差(Beck/Katz '95)，控制每个面板内的一阶相关性(Stata中的corr AR1选项)。“

如何控制R中每个面板中的一阶相关性？

以下是我到目前为止对我的数据所做的工作：

## run lm 
res.lm <- lm(total_FDI ~ ciri + human_cap + worker_rts + polity_4 + lag_total + market_size + income + econ_growth + log_trade + fixed_xr + fix_dollar + xr_fluct + english + lab_growth, data=D)
## run pcse
res.pcse <- pcse2(res.lm,groupN="c_code",groupT="year",pairwise=TRUE)

Answer 1

正如拉姆纳特提到的，pcse package将做Stata的xtpcse所做的事情。或者，您可以从plm package.使用vcovBK()函数。如果您选择后一种选项，请确保使用cluster='time'选项，这是Beck & Katz (1995)文章所建议的，也是Stata命令实现的。

pcse包运行良好，但也存在一些问题，使得许多直观的用户输入无法接受，尤其是在数据集不平衡的情况下。您可能想尝试重写我之前编写的函数。只需加载pcse包，加载pcse2函数，并按照pcse文档中的说明使用它。我的意思是，下面粘贴的函数比pcse提供的函数更干净、更灵活、更健壮。简单的基准测试也表明，我的版本可能比他们的快5到10倍，这可能对大数据集很重要。

祝好运!

library(Matrix)
pcse2 <- function(object, groupN, groupT, pairwise=TRUE){
  ## Extract basic model info
  groupT <- tail(as.character((match.call()$groupT)), 1)
  groupN <- tail(as.character((match.call()$groupN)), 1)
  dat <- eval(parse(text=object$call$data))

  ## Sanity checks
  if(!"lm" %in% class(object)){stop("Formula object must be of class 'lm'.")}
  if(!groupT %in% colnames(dat)){stop(paste(groupT, 'was not found in data', object$call$data))}
  if(!groupN %in% colnames(dat)){stop(paste(groupN, 'was not found in data', object$call$data))}
  if(anyDuplicated(paste(dat[,groupN], dat[,groupT]))>0){stop(paste('There are duplicate groupN-groupT observations in', object$call$data))}
  if(length(dat[is.na(dat[,groupT]),groupT])>0){stop('There are missing unit indices in the data.')}
  if(length(dat[is.na(dat[,groupN]),groupN])>0){stop('There are missing time indices in the data.')}

  ## Expand model frame to include groupT, groupN, resid columns.
  f <- as.formula(object$call$formula)
  f.expanded <- update.formula(f, paste(". ~ .", groupN, groupT, sep=" + "))
  dat.pcse <- model.frame(f.expanded, dat) 
  dat.pcse$e <- resid(object)  

  ## Extract basic model info (part II)
  N <- length(unique(dat.pcse[,groupN]))
  T <- length(unique(dat.pcse[,groupT]))
  nobs <- nrow(dat.pcse)
  is.balanced <- length(resid(object)) == N * T

  ## If balanced dataset, calculate as in Beck & Katz (1995)
  if(is.balanced){
    dat.pcse <- dat.pcse[order(dat.pcse[,groupN], dat.pcse[,groupT]),]
    X <- model.matrix(f, dat.pcse)
    E <- t(matrix(dat.pcse$e, N, T, byrow=TRUE))
    Omega <- kronecker((crossprod(E) / T), Matrix(diag(1, T)) )

  ## If unbalanced and pairwise, calculate as in Franzese (1996)
  }else if(pairwise==TRUE){
    ## Rectangularize
    rectangle <- expand.grid(unique(dat.pcse[,groupN]), unique(dat.pcse[,groupT]))
    names(rectangle) <- c(groupN, groupT)
    rectangle <- merge(rectangle, dat.pcse, all.x=TRUE)
    rectangle <- rectangle[order(rectangle[,groupN], rectangle[,groupT]),]
    valid <- ifelse(is.na(rectangle$e),0,1) 
    rectangle[is.na(rectangle)] <- 0
    X <- model.matrix(f, rectangle)
    X[valid==0,1] <- 0

    ## Calculate pcse
    E <- crossprod(t(matrix(rectangle$e, N, T, byrow=TRUE)))
    V <- crossprod(t(matrix(valid, N, T, byrow=TRUE)))
    if (length(V[V==0]) > 0){stop("Error! A CS-unit exists without any obs or without any obs in a common period with another CS-unit. You must remove that unit from the data passed to pcse().")}
    Omega <-  kronecker(E/V, Matrix(diag(1, T)))

  ## If unbalanced and casewise, caluate based on largest rectangular subset of data
  }else{ 
    ## Rectangularize
    rectangle <- expand.grid(unique(dat.pcse[,groupN]), unique(dat.pcse[,groupT]))
    names(rectangle) <- c(groupN, groupT)
    rectangle <- merge(rectangle, dat.pcse, all.x=TRUE)
    rectangle <- rectangle[order(rectangle[,groupN], rectangle[,groupT]),]
    valid <- ifelse(is.na(rectangle$e),0,1) 
    rectangle[is.na(rectangle)] <- 0
    X <- model.matrix(f, rectangle)
    X[valid==0,1] <- 0

    ## Keep only years for which we have the max number of observations
    large.panels <- by(dat.pcse, dat.pcse[,groupT], nrow) # How many valid observations per year?
    if(max(large.panels) < N){warning('There is no time period during which all units are observed. Consider using pairwise estimation.')}
    T.balanced <- names(large.panels[large.panels==max(large.panels)]) # Which years have max(valid observations)?
    T.casewise <- length(T.balanced)
    dat.balanced <- dat.pcse[dat.pcse[,groupT] %in% T.balanced,] # Extract biggest rectangular subset
    dat.balanced <- dat.balanced[order(dat.balanced[,groupN], dat.balanced[,groupT]),]
    e <- dat.balanced$e

    ## Calculate pcse as in Beck & Katz (1995)
    E <- t(matrix(dat.balanced$e, N, T.casewise, byrow=TRUE))
    Omega <- kronecker((crossprod(E) / T.casewise), Matrix(diag(1, T)))
  }

  ## Finish evaluation, clean and output
  salami <- t(X) %*% Omega %*% X
  bread <- solve(crossprod(X))
  sandwich <- bread %*% salami %*% bread
  colnames(sandwich) <- names(coef(object))
  row.names(sandwich) <- names(coef(object))
  pcse <- sqrt(diag(sandwich))
  b <- coef(object)
  tstats <- b/pcse
  df <- nobs - ncol(X)
  pval <- 2*pt(abs(tstats), df, lower.tail=FALSE)
  res <- list(vcov=sandwich, pcse=pcse, b=b, tstats=tstats, df=df, pval=pval, pairwise=pairwise, 
              nobs=nobs, nmiss=(N*T)-nobs, call=match.call())
  class(res) <- "pcse"
  return(res)
}

Answer 2

看看pcse package，它考虑了面板更正的标准错误。当然，您必须查看STATA中的文档，以找出所做的假设，并与pcse进行交叉验证。