Waldtest in R to get adjust F statistic with plm and result shown with stargazer?

问题:

I am working with an unbalanced short panel. Raw data: bankFull.xlsx

What I actually want is only get the regression results with two side fixed effects and robust S.E reported, which is very easy in Stata. I followed online tutorial but ran into some problem always with

# Adjust F statistic  wald_results <- waldtest(FE1, vcov = cov1) Error in model.matrix.pFormula(formula, data, rhs = 1, model = model,  :    NA in the individual index variable 

no matter how I adjusted the data! It almost drives me crazy.

here is my code:

bankFull <- openxlsx::read.xlsx("bankFull.xlsx",1)  attach(bankFull) library(plm)  FE1 = plm(  RoA ~               log(1+degreeNW)+               ln_assets+               log(no_of_board_members/staffNo)+               log(no_of_branch_covered_city)+                log(operation_year)+               `RoA-1`+               log(staffNo),             data = bankFull, index = c("name","year"),               effect="twoways",na.action = na.omit,             model= "within")  # robust S.E.----------- library(sandwich) library(lmtest)   # waldtest; see also coeftest. library(stargazer)  # Adjust standard errors cov1         <- vcovHC(FE1, type = "HC1") robust_se    <- sqrt(diag(cov1))   # Adjust F statistic  wald_results <- waldtest(FE1, vcov = cov1)  # show results. how can I get the F value? stargazer(FE1, FE1, type = "text",           se        = list(NULL, robust_se),           omit.stat = "f") 

Secondly, as the code shown, I use stargazer to demonstrate the results. I also need the adjusted F value to be shown in the table. Is there any option in the package that I can use?

回答1:

Edit: update information according to CRAN release 1.6-4 of plm

Use the CRAN version 1.6-4 of plm supports robust F tests for your model by function pwaldtest (this function was called Ftest in the development version but renamed prior to CRAN release).

Example:

data("Grunfeld", package = "plm") mod_fe <- plm(inv ~ value + capital, data = Grunfeld, model = "within") plm::pwaldtest(mod_fe, test = "F")   # with robust vcov plm::pwaldtest(mod_fe, test = "F", vcov = vcovHC(mod_fe)) plm::pwaldtest(mod_fe, test = "F", vcov = function(x) vcovHC(x, type = "HC3")) summary(mod_fe, vcov = vcovHC) 

To feed the robust values (robust standard errors, t- and p-values, F-value associated p-value) use arguments se, t, p and for the F test simply add.lines of the stargazer command (and omit the F statistic generated by stargazer by default). Here is a full example for what you want: http://jakeruss.com/cheatsheets/stargazer.html (section "Robust standard errors (replicating Stata’s robust option)").

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