data.firms <-read.csv("ES_firm_returns.csv",header=TRUE, sep = ";")
data.mkt <-read.csv("ES_market_returns.csv",header=TRUE, sep = ";")
data.firms$Date <- as.Date(data.firms$Date, format="%d.%m.%Y")
data.mkt$Date <- as.Date(data.mkt$Date, format="%d.%m.%Y")

returns <- merge(data.firms,data.mkt,by.x="Date",by.y="Date",all=F,sort=F)

event.dates <- data.frame(Adid = as.Date("2005-11-03"),
                          Bay = as.Date("2018-06-04"),
                          Eon = as.Date("2017-03-16"),
                          Fres = as.Date("2008-08-12"),
                          HeidC = as.Date("2008-09-15"),
                          Linde = as.Date("2012-07-09"),
                          Merck = as.Date("2007-01-22"),
                          RWE = as.Date("2011-12-05"),
                          Thys = as.Date("2017-09-25"),
                          VW = as.Date("2010-03-23"))

ID <- data.frame(Adid = which(returns$Date==event.dates$Adid, arr.ind=T),
                 Bay = which(returns$Date==event.dates$Bay, arr.ind=T),
                 Eon = which(returns$Date==event.dates$Eon, arr.ind=T),
                 Fres = which(returns$Date==event.dates$Fres, arr.ind=T),
                 HeidC = which(returns$Date==event.dates$HeidC, arr.ind=T),
                 Linde = which(returns$Date==event.dates$Linde, arr.ind=T),
                 Merck = which(returns$Date==event.dates$Merck, arr.ind=T),
                 RWE = which(returns$Date==event.dates$RWE, arr.ind=T),
                 Thys = which(returns$Date==event.dates$Thys, arr.ind=T),
                 VW = which(returns$Date==event.dates$VW, arr.ind=T))

ID[2,] <- ID[1,]-20 #Specifies the end of the estimation window
ID[3,] <- ID[2,]-249 #Start of the estimation window
ID[4,] <- ID[1,]-5 # ID4 = Start of the event Windows, so 5 days prior to the event date

est.windows <- data.frame(row.names = c("start", "end"))
    N <- 10
    for (i in 1:N)
        est.windows[i] <- c(returns$Date[ID[3,i]],returns$Date[ID[2,i]])

    est.returns <- data.frame(row.names = c(1:250))
    for (i in 1:N)
        est.returns[i] <- subset(returns[i+1], returns$Date>=est.windows[1,i]&returns$Date<=est.windows[2,i])

    for (i in 1:N)
      est.returns[N+i] <- subset(returns[ncol(returns)], returns$Date>=est.windows[1,i]&returns$Date<=est.windows[2,i])

event.windows <- data.frame(row.names = c("(0)","(-5,0)"))
for (i in 1:N)
  event.windows[i] <- c(returns$Date[ID[1,i]],returns$Date[ID[4,i]])

d <- 1
event.returns <- data.frame(row.names = c(1:d))
for (i in 1:N)

  event.returns[i] <- subset(returns[i+1], returns$Date==event.windows[1,i])
for (i in 1:N)
  event.returns[N+i] <- subset(returns[ncol(returns)], returns$Date==event.windows[1,i])

event.returns$AR_Adid <- event.returns$Adid - (est.reg.Adid$coefficients[1]+est.reg.Adid$coefficients[2]*event.returns$MKT) #This line of code for every company...

d <- 6
event.returns.50 <- data.frame(row.names = c(1:d))

for (i in 1:N)
  event.returns.50[i] <- subset(returns[i+1], returns$Date>=event.windows[2,i]&returns$Date<=event.windows[1,i])

for (i in 1:N)
  event.returns.50[N+i] <- subset(returns[ncol(returns)], returns$Date>=event.windows[2,i]&returns$Date<=event.windows[1,i])

event.returns.50$AR_Adid <- event.returns.50$Adid - (est.reg.Adid$coefficients[1]+est.reg.Adid$coefficients[2]*event.returns.50$MKT)
#This line of code for every company