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Panel Data Regression: Reliable Standard Errors

my problem is this: I get NA where I need to get some values ​​when calculating reliable standard errors.

I am trying to do a regression with a fixed effect using standard errors. For this, I follow Arai (2011) , which on p. 3 follows Stock / Watson (2006) (later published in Econometrica , for those with access). I would like to adjust the degrees of freedom by (M/(M-1)*(N-1)/(NK) against the downward shift, since my number of clusters is finite, and I have unbalanced data.

Similar issues were posted before [ 1 , 2 ] on StackOverflow and related issues [ 3 ] on CrossValidated.

Arai (and the answer in the 1st link) uses the following code for functions (below I give my data below):

 gcenter <- function(df1,group) { variables <- paste( rep("C", ncol(df1)), colnames(df1), sep=".") copydf <- df1 for (i in 1:ncol(df1)) { copydf[,i] <- df1[,i] - ave(df1[,i], group,FUN=mean)} colnames(copydf) <- variables return(cbind(df1,copydf))} # 1-way adjusting for clusters clx <- function(fm, dfcw, cluster){ # R-codes (www.r-project.org) for computing # clustered-standard errors. Mahmood Arai, Jan 26, 2008. # The arguments of the function are: # fitted model, cluster1 and cluster2 # You need to install libraries `sandwich' and `lmtest' # reweighting the var-cov matrix for the within model library(sandwich);library(lmtest) M <- length(unique(cluster)) N <- length(cluster) K <- fm$rank dfc <- (M/(M-1))*((N-1)/(NK)) uj <- apply(estfun(fm),2, function(x) tapply(x, cluster, sum)); vcovCL <- dfc*sandwich(fm, meat=crossprod(uj)/N)*dfcw coeftest(fm, vcovCL) }

where gcenter calculates the deviations from the mean (fixed effect). Then I continue and do the regression with DS_CODE , which is my cluster variable (I named my data data).

 centerdata <- gcenter(data, data$DS_CODE) datalm <- lm(C.L1.retE1M ~ C.MCAP_SEC + C.Impact_change + C.Mom + C.BM + C.PD + C.CashGen + C.NITA + C.PE + C.PEdummy + factor(DS_CODE), data=centerdata) M <- length(unique(data$DS_CODE)) dfcw <- datalm$df / (datalm$df - (M-1))

and want to calculate

 clx(datalm, dfcw, data$DS_CODE)

However, when I want to calculate uj (see clx formula above) for variance, I only get at the beginning some values ​​for my regressors, and then a lot of zeros. If this input uj is used for dispersion, the result is only NAs .

My details

Since my data may have a special structure, and I cannot understand the problem, I post it all as a link Hotmail The reason is that with other data (taken from Arai (2011)) my problem does not arise. Sorry in advance for the mess, but I would really appreciate it if you could take a look at it nonetheless. The file is a 5 MB .txt file containing pure data.

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2 answers

After some time, it works for me and gives me:

  Estimate Std. Error t value Pr(>|t|) (Intercept) 4.5099e-16 5.2381e-16 0.8610 0.389254 C.MCAP_SEC -5.9769e-07 1.2677e-07 -4.7149 2.425e-06 *** C.Impact_change -5.3908e-04 7.5601e-05 -7.1306 1.014e-12 *** C.Mom 3.7560e-04 3.3378e-03 0.1125 0.910406 C.BM -1.6438e-04 1.7368e-05 -9.4645 < 2.2e-16 *** C.PD 6.2153e-02 3.8766e-02 1.6033 0.108885 C.CashGen -2.7876e-04 1.4031e-02 -0.0199 0.984149 C.NITA -8.1792e-02 3.2153e-02 -2.5438 0.010969 * C.PE -6.6170e-06 4.0138e-06 -1.6485 0.099248 . C.PEdummy 1.3143e-02 4.8864e-03 2.6897 0.007154 ** factor(DS_CODE)130324 -5.2497e-16 5.2683e-16 -0.9965 0.319028 factor(DS_CODE)130409 -4.0276e-16 5.2384e-16 -0.7689 0.441986 factor(DS_CODE)130775 -4.4113e-16 5.2424e-16 -0.8415 0.400089 ...

This leaves us with a question why this is not for you. I assume this has something to do with the format of your data. Is everything numerical? I converted the column classes and for me it looks like this:

 str(dat) 'data.frame': 48251 obs. of 12 variables: $ DS_CODE : chr "902172" "902172" "902172" "902172" ... $ DNEW : num 2e+05 2e+05 2e+05 2e+05 2e+05 ... $ MCAP_SEC : num 78122 71421 81907 80010 82462 ... $ NITA : num 0.135 0.135 0.135 0.135 0.135 ... $ CashGen : num 0.198 0.198 0.198 0.198 0.198 ... $ BM : num 0.1074 0.1108 0.097 0.0968 0.0899 ... $ PE : num 57 55.3 63.1 63.2 68 ... $ PEdummy : num 0 0 0 0 0 0 0 0 0 0 ... $ L1.retE1M : num -0.72492 0.13177 0.00122 0.07214 -0.07332 ... $ Mom : num 0 0 0 0 0 ... $ PD : num 5.41e-54 1.51e-66 3.16e-80 2.87e-79 4.39e-89 ... $ Impact_change: num 0 -10.59 -10.43 0.7 -6.97 ...

What returns str(data) for you?

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The plm package can evaluate clustered SEs for group regressions. The source data is no longer available, so here is an example of using dummy data.

 require(foreign) require(plm) require(lmtest) test <- read.dta("http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.dta") fpm <- plm(y ~ x, test, model='pooling', index=c('firmid', 'year')) ##Arellano clustered by *group* SEs > coeftest(fpm, vcov=function(x) vcovHC(x, cluster="group", type="HC0")) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.029680 0.066939 0.4434 0.6575 x 1.034833 0.050540 20.4755 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

If you use lm models (instead of plm ), the multiwayvcov package may help.

 library("lmtest") library("multiwayvcov") data(petersen) m1 <- lm(y ~ x, data = petersen) > coeftest(m1, vcov=function(x) cluster.vcov(x, petersen[ , c("firmid")], df_correction=FALSE)) t test of coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.029680 0.066939 0.4434 0.6575 x 1.034833 0.050540 20.4755 <2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

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Source: https://habr.com/ru/post/916293/

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