fixed effects in r in r

To estimate fixed effects in R, we can use the lm() function with the factor() function to create dummy variables for each of the fixed effects. Here's an example:

main.r
# Load the plm package for panel data analysis
library(plm)

# Load the Grunfeld data set from the plm package
data("Grunfeld")

# Estimate a fixed effects model using the lm function
fe_model <- lm(inv ~ value + capital + factor(firm), data=Grunfeld)

# Alternatively, we can use the plm function to estimate fixed effects
plm_model <- plm(inv ~ value + capital, data=Grunfeld, index=c("firm", "year"), model="within")
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In this example, we estimate a fixed effects model for the Grunfeld data set, which is a panel data set with observations for 10 firms over a 20-year period. The variable inv represents investment, value represents market value of the firm, and capital represents physical capital. We create dummy variables for each of the 10 firms using the factor() function and include them as fixed effects in the model. We can also use the plm() function from the plm package to estimate fixed effects, which requires specifying the panel index and the model type.