CODEWITHSUNDEEP
rpanelpanel-dataplm
LucaCoding
LucaCoding

Reputation: 65

Maximum Likelihood Estimation -MLE- with plm package in R

I have unbalanced panel data and I want to fit this type of regression:

Pr(y=1|xB) = G(xB+a)

where "y" is a binary variable, "x" vector of explanatory variables and "B" my coeff.

I want to implement random effect model with maximum likelihood estimation, however I didn't understand what I need to change in the plm function (of package plm) CRAN guide (vignette). As far I used this code:

library(plm)
p_finale <- plm.data(p_finale, index=c("idnumber","Year"))
attach(p_finale)
y <- (TotalDebt_dummy)
X_tot <- cbind(Size,ln_Age,liquidity,Asset_Tangibility,profitability,growth, sd_cf_risk1, family_dummy,family_manager,
               sd_cf_risk1*family_dummy,
               Ateco_A,Ateco_C,Ateco_D,Ateco_E,Ateco_F,Ateco_G,Ateco_H,Ateco_I,Ateco_J,Ateco_M,Ateco_N,
               Ateco_Q,Ateco_R)

model1 <- plm(y~X_tot+factor(Year),data = p_finale, model="random")

I included the whole code, but the only thing I believe needs to be changed is the last row in plm.

Upvotes: 0

Views: 646

Answers (1)

Helix123
Helix123

Reputation: 3677

Function plm from package plm does not use a maximum-likelihood approach for model estimation. It uses a GLS approach as is common in econometrics.

Please see the section about plm versus nlme and lme4 in the package's first vignette ("Panel data econometrics with R: the plm package" (https://cran.rstudio.com/web/packages/plm/vignettes/A_plmPackage.html). The section explains the differences between the appraoches and has code examples for boths (and refers to packages nlme and lme4 for the maximum-likelihood approach).

Upvotes: 1

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