#1. Install and load the necessary packages:
  
install.packages(“vars”)
install.packages(“sandwich”)
install.packages(“zoo”)

library(vars)
library(sandwich)
library(zoo)

library(readxl)

data <- read_excel("C:/Users/Yusuf/Downloads/data.xlsx")
View(data)


#2. Convert your time series data into a zoo object (Assuming your data frame “data” already consists of Date, RET_SI, RET_OP, and RET_ER columns):
  
df <- zoo(data[, -1], order.by = as.Date(data$Date))


#3. Define the time periods you want to analyze:
  
pre_covid <- window(df, start = as.Date("2014-01-03"), end = as.Date("2019-12-30"))
covid_period <- window(df, start = as.Date("2020-01-30"), end = as.Date("2021-02-25"))
war_period <- window(df, start = as.Date("2022-02-24"), end = as.Date("2023-04-28"))
overall_period <- df


#4. Define a function to determine the optimal lag length using AIC and SBC:

  optimal_lag_order <- function(data, max_lag) {
    ic_values <- VARselect(data, lag.max = max_lag)
    return(ic_values$selection)
  }

#5. Determine the optimal lag length for each period (specify the maximum lag length you want to consider, e.g., 5):
  
max_lag = 5 # Replace with the maximum lag length you want to consider
pre_covid_lags <- optimal_lag_order(pre_covid, max_lag)
covid_period_lags <- optimal_lag_order(covid_period, max_lag)
war_period_lags <- optimal_lag_order(war_period, max_lag)
overall_period_lags <- optimal_lag_order(overall_period, max_lag)


#6. Define a function to estimate the VAR model and compute the robust standard errors with the sandwich estimator:
  
  compute_VAR_robust <- function(data, p) {
    var_model <- VAR(data, p = p)
    vcov_matrix <- sandwich(var_model)
    robust_se <- sqrt(diag(vcov_matrix))
    return(list(var_model = var_model, robust_se = robust_se))
  }


#7. Estimate the VAR model for each period using the optimal lag length:
  
pre_covid_results <- compute_VAR_robust(pre_covid, pre_covid_lags[1])
covid_period_results <- compute_VAR_robust(covid_period, covid_period_lags[1])
war_period_results <- compute_VAR_robust(war_period, war_period_lags[1])
overall_period_results <- compute_VAR_robust(overall_period, overall_period_lags[1])


#8. You now have four sets of results that include the estimated VAR models with optimal lag lengths and the robust standard errors. To access the VAR models and the robust standard errors for each period, you can do the following:
  
# VAR models
pre_covid_var <- pre_covid_results$var_model
covid_period_var <- covid_period_results$var_model
war_period_var <- war_period_results$var_model
overall_period_var <- overall_period_results$var_model

9# Robust standard errors
pre_covid_robust_se <- pre_covid_results$robust_se
covid_period_robust_se <- covid_period_results$robust_se
war_period_robust_se <- war_period_results$robust_se
overall_period_robust_se <- overall_period_results$robust_se

#10. To view the VAR results, you can use the summary function for each VAR model:
  
#10.1 Without robust standard errors
summary(pre_covid_var)
summary(covid_period_var)
summary(war_period_var)
summary(overall_period_var)

#10.2 With robust standard errors
# First, you need to manually replace the original standard errors with the robust ones:
pre_covid_var_robust <- pre_covid_var
covid_period_var_robust <- covid_period_var
war_period_var_robust <- war_period_var
overall_period_var_robust <- overall_period_var

#10.3 Replace original standard errors with the robust ones
for (i in seq_along(pre_covid_var_robust$varresult[[i]]$varresult[[i]]$varresult[[i]]$varresult[[i]]$coefficients[, 2] <- overall_period_robust_se}

#10.4 Now you can view the results with robust standard errors
summary(pre_covid_var_robust)
summary(covid_period_var_robust)
summary(war_period_var_robust)
summary(overall_period_var_robust)

Here is your previous response. R is showing an error for comments 10.3 and 10.4. Avoid using the "$" sign because the browser breaks the "$" sign into "&lt;" which makes it hard to understand the real code. So, replace "$" with "*" in every R command for me. I will recognize it. Therefore, correct the entire code as per my requirements again.