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############ WOOLDRIDGE ###############
# function to estimate Wooldridge #
prodestWRDG_GMM <- function(Y, fX, sX, pX, idvar, timevar, cX = NULL, tol = 1e-100){
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)
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
Y <- data$Y; dY = c(data$Y, data$Y); X1 = cbind(1, data$fX, data$regvars)
X2 = cbind(1, data$fX, data$sX, 1, data$lagregvars)
Z1 = cbind(1, data$fX, data$regvars)
Z2 = cbind(1, data$lag.fX, data$sX, data$lagregvars) # define the data and the matrices
numR = 1 + fnum + snum + cnum # number of restricted columns
numU1 <- ncol(X1) - numR # number of unrestricted cols of X1
numU2 <- ncol(X2) - numR # number of unrestricted cols of X2
N <- nrow(X1)
dX <- rbind( cbind( X1, matrix(0, N, numU2 ) ),
cbind( X2[, 1 : numR], matrix(0 , N, numU1), X2[,(numR + 1) : ncol(X2)]) ) # generate a "quasi-block" matrix with common columns NON-BLOCK
Z <- as.matrix(bdiag(Z1, Z2))
W <- solve((t(Z) %*% Z)) * diag(ncol(Z)) # unadjusted, independent
betas.1st <- solve(t(dX) %*% Z %*% W %*% t(Z) %*% dX, tol = tol) %*%
t(dX) %*% Z %*% W %*% t(Z) %*% dY # 1st stage GMM parameters
W.star <- weightM(Y = Y, X1 = X1, X2 = X2, Z1 = Z1, Z2 = Z2,
betas = betas.1st, numR = (fnum + snum + cnum + 1)) # compute optimal weighting matrix
betas.2nd <- solve(t(dX) %*% Z %*% W.star %*% t(Z) %*% dX, tol = tol) %*%
t(dX) %*% Z %*% W.star %*% t(Z) %*% dY # 2nd step
st.errors <- weightM(Y = Y, X1 = X1, X2 = X2, Z1 = Z1, Z2 = Z2,
betas = betas.2nd, numR = (fnum + snum + cnum + 1), SE = TRUE) # compute standard errors
res.names <- c(colnames(fX, do.NULL = FALSE, prefix = 'fX'),
colnames(sX, do.NULL = FALSE, prefix = 'sX') ) # generate the list of names for results
if (!is.null(cX)) {res.names <- c(res.names, colnames(cX, do.NULL = FALSE, prefix = 'cX'))} # add cX names to the results' names
betapar <- betas.2nd[2: (snum + fnum + cnum + 1)]
betase <- st.errors[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 = 'WRDG.GMM', boot.repetitions = NA, elapsed.time = elapsed.time, theta0 = NA,
opt = NA, opt.outcome = NULL),
Data = list(Y = Y, free = fX, state = sX, proxy = pX, control = cX, idvar = idvar, timevar = timevar),
Estimates = list(pars = betapar, std.errors = betase))
return(out)
}
# end of Wooldridge #
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