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############ ROBINSON/WOOLDRIDGE - IV REGRESSION ###############
# function to estimate Wooldridge #
prodestROB <- function(Y, fX, sX, pX, idvar, timevar, cX = NULL){
Start = Sys.time() # start tracking time
Y <- checkM(Y) # change all input to matrix
fX <- checkM(fX)
sX <- checkM(sX)
pX <- checkM(pX)
idvar <- checkM(idvar)
timevar <- checkM(timevar)
opt = 'optim' # this is going to be changed once the routine will work with DEoptim and solnp
snum <- ncol(sX) # find the number of input variables
fnum <- ncol(fX)
if (!is.null(cX)) {cX <- checkM(cX); cnum <- ncol(cX)} else {cnum <- 0} # if is there any control, take it into account, else fix the number of controls to 0
lag.fX = fX # generate fX lags
for (i in 1:fnum) {
lag.fX[, i] = lagPanel(fX[, i], idvar = idvar, timevar = timevar)
}
polyframe <- data.frame(sX,pX) # vars to be used in polynomial approximation
regvars <- cbind(model.matrix( ~.^2-1, data = polyframe), sX^2, pX^2) # generate a polynomial of the desired level
lagregvars <- regvars
for (i in 1:dim(regvars)[2]) {
lagregvars[, i] <- lagPanel(idvar = idvar, timevar = timevar, regvars[ ,i])
}
data <- model.frame(Y ~ fX + sX + lag.fX + regvars + lagregvars + idvar + timevar) # data.frame of usable observations --> regvars
iv.out <- ivreg(Y ~ fX + sX + lagregvars | lag.fX + sX + lagregvars, data = data)
res.names <- c(colnames(fX, do.NULL = FALSE, prefix = 'fX'),
colnames(sX, do.NULL = FALSE, prefix = 'sX') ) # generate the list of names for results
betapar <- iv.out$coefficients[2: (snum + fnum + cnum + 1)]
betase <- coef(summary(iv.out))[, 2][2: (snum + fnum + cnum + 1)]
names(betapar) <- res.names # change results' names
names(betase) <- res.names # change results' names
elapsed.time = Sys.time() - Start # total running time
out <- new("prod",
Model = list(method = 'ROB-IV', FSbetas = NULL, boot.repetitions = NA, elapsed.time = elapsed.time, theta0 = NA,
opt = NA, opt.outcome = NULL, nCores = 1),
Data = list(Y = Y, free = fX, state = sX, proxy = pX, control = cX, idvar = idvar, timevar = timevar,
FSresiduals = NULL),
Estimates = list(pars = betapar, std.errors = betase))
return(out)
}
## end of prodest Robinson-Wooldridge ##
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