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# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# A copy of the GNU General Public License is available at
# http://www.r-project.org/Licenses/
getModel <- function(object, ...)
{
UseMethod("getModel")
}
getModel.tsls <- function(object, ...)
{
obj <- getModel.baseGmm(object, ...)
return(obj)
}
getModel.sysGmm <- function(object, ...)
{
if (object$commonCoef & !is.null(object$crossEquConst))
{
object$commonCoef <- FALSE
warning("When crossEquConst is not NULL, commonCoef=TRUE is ignore and set to FALSE")
}
object$allArg <- c(object, ...)
object$formula <- list(g=object$g, h=object$h)
if (!is.list(object$g))
stop("g must be list of formulas")
if (length(object$g) == 1)
stop("For single equation GMM, use the function gmm()")
if (!all(sapply(1:length(object$g), function(i) is(object$g[[i]], "formula"))))
stop("g must be a list of formulas")
if (!is.list(object$h))
{
if(!is(object$h, "formula"))
stop("h is either a list of formulas or a formula")
else
object$h <- list(object$h)
} else {
if (!all(sapply(1:length(object$h), function(i) is(object$h[[i]], "formula"))))
stop("h is either a list of formulas or a formula")
}
if (length(object$h) == 1)
{
object$h <- rep(object$h, length(object$g))
typeDesc <- "System Gmm with common instruments"
typeInst <- "Common"
} else {
if (length(object$h) != length(object$g))
stop("The number of formulas in h should be the same as the number of formulas in g, \nunless the instruments are the same in each equation, \nin which case the number of equations in h should be 1")
typeDesc <- "System Gmm"
typeInst <- "nonCommon"
}
if (object$commonCoef)
typeDesc <- paste(typeDesc, " (Common Coefficients)")
dat <- lapply(1:length(object$g), function(i) try(getDat(object$g[[i]], object$h[[i]], data = object$data,
error=!object$commonCoef), silent=TRUE))
chk <- sapply(1:length(dat), function(i) any(class(dat[[i]]) == "try-error"))
chk <- which(chk)
mess <- vector()
for (i in chk)
{
mess <- paste(mess, "\nSystem of equations:", i, "\n###############\n", sep="")
mess <- paste(mess, attr(dat[[i]], "condition")[[1]])
}
if (length(chk)>0)
stop(mess)
if (is.null(names(object$g)))
names(dat) <- paste("System_", 1:length(dat), sep="")
else
names(dat) <- names(object$g)
object$gradv <- .DmomentFct_Sys
object$formula <- list(g=object$g, h=object$h)
if (!all(sapply(1:length(dat), function(i) dat[[i]]$ny == 1)))
stop("The number of dependent variable must be one in every equation")
clname <- "sysGmm.twoStep.formula"
object$x <- dat
namex <- lapply(1:length(dat), function(i) colnames(dat[[i]]$x[,2:(1+dat[[i]]$k), drop=FALSE]))
nameh <- lapply(1:length(dat), function(i) colnames(dat[[i]]$x[,(2+dat[[i]]$k):(1+dat[[i]]$k+dat[[i]]$nh), drop=FALSE]))
namey <- lapply(1:length(dat), function(i) colnames(dat[[i]]$x[,1, drop=FALSE]))
object$namesCoef <- namex
namesgt <- lapply(1:length(dat), function(i) paste(namey[[i]], "_", nameh[[i]], sep=""))
object$namesgt <- namesgt
object$namesy <- namey
attr(object$x,"ModelType") <- "linear"
#for (i in 1:length(object$x))
# attr(object$x[[i]], c("linear")) <- attr(object$x, "modelType")
attr(object$x, "k") <- lapply(1:length(dat), function(i) length(object$namesCoef[[i]]))
attr(object$x, "q") <- lapply(1:length(dat), function(i) length(object$namesgt[[i]]))
attr(object$x, "n") <- lapply(1:length(dat), function(i) nrow(object$x[[i]]$x))
object$TypeGmm <- class(object)
attr(object$x, "weight") <- list(w=object$weightsMatrix,
centeredVcov=object$centeredVcov)
attr(object$x, "weight")$WSpec <- list()
attr(object$x, "weight")$WSpec$sandwich <- list(kernel = object$kernel, bw = object$bw,
prewhite = object$prewhite,
ar.method = object$ar.method,
approx = object$approx, tol = object$tol)
attr(object$x, "weight")$vcov <- object$vcov
k <- lapply(1:length(dat), function(i) dat[[i]]$k)
q <- lapply(1:length(dat), function(i) dat[[i]]$nh)
df <- lapply(1:length(dat), function(i) dat[[i]]$nh-dat[[i]]$k)
k2 <- do.call(c,k)
if (object$commonCoef | !is.null(object$crossEquConst))
{
if (!all(k2==k2[1]))
stop("In a common coefficient model the number of regressors is the same in each equation")
if (object$commonCoef)
totK <- k2[1]
else
totK <- length(dat)*(k2[1]-length(object$crossEquConst)) + length(object$crossEquConst)
if (sum(do.call(c,q))<totK)
stop("The number of moment conditions is less than the number of coefficients")
if (!is.null(object$crossEquConst))
{
object$crossEquConst <- sort(object$crossEquConst)
if (length(object$crossEquConst) == k2[1])
if (all(object$crossEquConst==(1:k2[1])))
{
object$crossEquConst <- NULL
object$commonCoef <- TRUE
}
}
}
attr(object$x, "sysInfo") <- list(k=k, df=df, q=q, typeInst=typeInst, typeDesc=typeDesc, commonCoef=object$commonCoef,
crossEquConst=object$crossEquConst)
object$g <- .momentFct_Sys
class(object) <- clname
return(object)
}
getModel.constGmm <- function(object, ...)
{
class(object) <- "baseGmm"
obj <- getModel(object)
if (!is.null(object$t0))
{
if (!is.null(dim(object$eqConst)))
stop("When t0 is provided, eqConst must be a vector which indicates which parameters to fix")
if (length(object$eqConst)>=length(object$t0))
stop("Too many constraints; use evalGmm() if all coefficients are fixed")
if (is.character(object$eqConst))
{
if (is.null(names(object$t0)))
stop("t0 must be a named vector if you want eqConst to be names")
if (any(!(object$eqConst %in% names(object$t0))))
stop("Wrong coefficient names in eqConst")
object$eqConst <- sort(match(object$eqConst,names(object$t0)))
}
restTet <- object$t0[object$eqConst]
obj$t0 <- object$t0[-object$eqConst]
object$eqConst <- cbind(object$eqConst,restTet)
} else {
if (is.null(dim(object$eqConst)))
stop("When t0 is not provided, eqConst must be a 2xq matrix")
}
attr(obj$x, "eqConst") <- list(eqConst = object$eqConst)
rownames(attr(obj$x, "eqConst")$eqConst) <- obj$namesCoef[object$eqConst[,1]]
object$eqConst <- attr(obj$x, "eqConst")$eqConst
if(is(object$g, "formula"))
{
if (obj$x$ny>1)
stop("Constrained GMM not implemented yet for system of equations")
if (obj$x$k<=0)
stop("Nothing to estimate")
}
obj$eqConst <- object$eqConst
attr(obj$x, "k") <- attr(obj$x, "k")-nrow(object$eqConst)
obj$namesCoef <- obj$namesCoef[-object$eqConst[,1]]
obj$type <- paste(obj$type,"(with equality constraints)",sep=" ")
mess <- paste(rownames(object$eqConst), " = " , object$eqConst[,2], "\n",collapse="")
mess <- paste("#### Equality constraints ####\n",mess,"##############################\n\n",sep="")
obj$specMod <- mess
return(obj)
}
getModel.baseGmm <- function(object, ...)
{
object$allArg <- c(object, list(...))
if(is(object$g, "formula"))
{
object$gradv <- .DmomentFct
object$gradvf <- FALSE
dat <- getDat(object$g, object$x, data = object$data)
if(is.null(object$weightsMatrix))
{
clname <- paste(class(object), ".", object$type, ".formula", sep = "")
} else {
clname <- "fixedW.formula"
object$type <- "One step GMM with fixed W"
}
object$x <- dat
object$gform<-object$g
namex <- colnames(dat$x[,(dat$ny+1):(dat$ny+dat$k), drop=FALSE])
nameh <- colnames(dat$x[,(dat$ny+dat$k+1):(dat$ny+dat$k+dat$nh), drop=FALSE])
if (dat$ny > 1)
{
namey <- colnames(dat$x[,1:dat$ny, drop=FALSE])
object$namesCoef <- paste(rep(namey, dat$k), "_",
rep(namex, rep(dat$ny, dat$k)), sep = "")
object$namesgt <- paste(rep(namey, dat$nh), "_",
rep(nameh, rep(dat$ny, dat$nh)), sep = "")
} else {
object$namesCoef <- namex
object$namesgt <- nameh
}
attr(object$x,"ModelType") <- "linear"
attr(object$x, "k") <- object$x$k
attr(object$x, "q") <- object$x$ny*object$x$nh
attr(object$x, "n") <- NROW(object$x$x)
attr(object$x, "namesgt") <- object$namesgt
} else {
attr(object$x,"ModelType") <- "nonlinear"
attr(object$x, "momentfct") <- object$g
if (object$optfct == "optimize")
attr(object$x, "k") <- 1
else
attr(object$x, "k") <- length(object$t0)
attr(object$x, "q") <- NCOL(gt <- object$g(object$t0, object$x))
attr(object$x, "n") <- NROW(gt)
if (object$optfct == "optimize")
{
object$namesCoef <- "Theta1"
} else {
if(is.null(names(object$t0)))
object$namesCoef <- paste("Theta[" ,1:attr(object$x, "k"), "]", sep = "")
else
object$namesCoef <- names(object$t0)
}
if(is.null(object$weightsMatrix))
{
clname <- paste(class(object), "." ,object$type, sep = "")
} else {
clname <- "fixedW"
object$type <- "One step GMM with fixed W"
attr(object$x, "weight")$w <- object$weightsMatrix
}
if (!is.function(object$gradv))
{
object$gradvf <- FALSE
} else {
attr(object$x, "gradv") <- object$gradv
object$gradvf <- TRUE
}
object$gradv <- .DmomentFct
}
object$TypeGmm <- class(object)
attr(object$x, "weight") <- list(w=object$weightsMatrix,
centeredVcov=object$centeredVcov)
attr(object$x, "weight")$WSpec <- list()
attr(object$x, "weight")$WSpec$sandwich <- list(kernel = object$kernel, bw = object$bw,
prewhite = object$prewhite,
ar.method = object$ar.method,
approx = object$approx, tol = object$tol)
attr(object$x, "weight")$vcov <- object$vcov
attr(object$x, "mustar") <- object$mustar
object$g <- .momentFct
class(object) <- clname
return(object)
}
getModel.constGel <- function(object, ...)
{
class(object) <- "baseGel"
obj <- getModel(object)
if (!is.null(dim(object$eqConst)))
stop("eqConst must be a vector which indicates which parameters to fix")
if (length(object$eqConst)>=length(object$tet0))
stop("Too many constraints; use evalGel() if all coefficients are fixed")
if (is.character(object$eqConst))
{
if (is.null(names(object$tet0)))
stop("tet0 must be a named vector if you want eqConst to be names")
if (any(!(object$eqConst %in% names(object$tet0))))
stop("Wrong coefficient names in eqConst")
object$eqConst <- sort(match(object$eqConst,names(object$tet0)))
}
restTet <- object$tet0[object$eqConst]
obj$tet0 <- object$tet0[-object$eqConst]
object$eqConst <- cbind(object$eqConst,restTet)
attr(obj$x, "eqConst") <- list(eqConst = object$eqConst)
rownames(attr(obj$x, "eqConst")$eqConst) <- obj$namesCoef[object$eqConst[,1]]
object$eqConst <- attr(obj$x, "eqConst")$eqConst
if(is(object$g, "formula"))
{
if (obj$x$ny>1)
stop("Constrained GMM not implemented yet for system of equations")
}
obj$eqConst <- object$eqConst
attr(obj$x, "k") <- attr(obj$x, "k")-nrow(object$eqConst)
obj$namesCoef <- obj$namesCoef[-object$eqConst[,1]]
obj$typeDesc <- paste(obj$typeDesc,"(with equality constraints)",sep=" ")
mess <- paste(rownames(object$eqConst), " = " , object$eqConst[,2], "\n",collapse="")
mess <- paste("#### Equality constraints ####\n",mess,"##############################\n\n",sep="")
obj$specMod <- mess
return(obj)
}
getModel.baseGel <- function(object, ...)
{
object$allArg <- c(object, list(...))
object$allArg$weights <- NULL
object$allArg$call <- NULL
if(is(object$g, "formula"))
{
dat <- getDat(object$g, object$x, data = object$data)
k <- dat$k
if (is.null(object$tet0))
{
if (!is.null(object$eqConst))
stop("You have to provide tet0 with equality constrains")
if (object$optfct == "optimize")
stop("For optimize, you must provide the 2x1 vector tet0")
res0 <- gmm(object$g, object$x, data=object$data)
object$tet0 <- res0$coefficients
if (object$smooth)
gt <- res0$gt
} else {
if (object$optfct == "optimize")
{
if (k != 1)
stop("optimize() is for univariate optimization")
if (length(object$tet0) != 2)
stop("For optimize(), tet0 must be a 2x1 vector")
} else {
if (k != length(object$tet0))
stop("The number of starting values does not correspond to the number of regressors")
}
if (object$smooth)
gt <- gmm(object$g, object$x, data=object$data)$gt
}
clname <- paste(class(object), ".modFormula", sep = "")
object$gradv <- .DmomentFct
object$gradvf <- FALSE
object$x <- dat
object$gform<-object$g
namex <- colnames(dat$x[,(dat$ny+1):(dat$ny+dat$k), drop=FALSE])
nameh <- colnames(dat$x[,(dat$ny+dat$k+1):(dat$ny+dat$k+dat$nh), drop=FALSE])
if (dat$ny > 1)
{
namey <- colnames(dat$x[,1:dat$ny])
namesCoef <- paste(rep(namey, dat$k), "_", rep(namex, rep(dat$ny, dat$k)), sep = "")
object$namesgt <- paste(rep(namey, dat$nh), "_", rep(nameh, rep(dat$ny, dat$nh)), sep = "")
} else {
namesCoef <- namex
object$namesgt <- nameh
}
if (is.null(names(object$tet0)))
object$namesCoef <- namesCoef
else
object$namesCoef <- names(object$tet0)
attr(object$x,"ModelType") <- "linear"
attr(object$x, "k") <- k
attr(object$x, "q") <- object$x$ny*object$x$nh
attr(object$x, "n") <- NROW(object$x$x)
} else {
if (is.null(object$tet0))
stop("You must provide the starting values tet0 for nonlinear moments")
if(any(object$optfct == c("optim", "nlminb")))
k <- length(object$tet0)
else
k <- 1
attr(object$x,"ModelType") <- "nonlinear"
attr(object$x, "momentfct") <- object$g
attr(object$x, "k") <- k
attr(object$x, "q") <- NCOL(gt <- object$g(object$tet0, object$x))
attr(object$x, "n") <- NROW(gt)
if(is.null(names(object$tet0)))
object$namesCoef <- paste("Theta[" ,1:attr(object$x, "k"), "]", sep = "")
else
object$namesCoef <- names(object$tet0)
if (!is.function(object$gradv) | object$smooth)
{
object$gradvf <- FALSE
} else {
attr(object$x, "gradv") <- object$gradv
object$gradvf <- TRUE
}
object$gradv <- .DmomentFct
if (object$smooth)
gt <- gmm(object$g, object$x, object$tet0, wmatrix = "ident", ...)$gt
clname <- paste(class(object), ".mod", sep = "")
}
if (object$smooth)
{
if (is.function(object$gradv))
warning("The provided gradv is not used when smooth=TRUE",
call. = FALSE)
if(object$kernel == "Truncated")
{
object$wkernel <- "Bartlett"
object$k1 <- 2
object$k2 <- 2
}
if(object$kernel == "Bartlett")
{
object$wkernel <- "Parzen"
object$k1 <- 1
object$k2 <- 2/3
}
gt <- scale(gt, scale=FALSE)
class(gt) <- "gmmFct"
if (is.function(object$bw))
object$bwVal <- object$bw(gt, kernel = object$wkernel, prewhite = object$prewhite,
ar.method = object$ar.method, approx = object$approx)
else
object$bwVal <- object$bw
object$w <- smoothG(gt, bw = object$bwVal, kernel = object$kernel, tol = object$tol_weights)$kern_weights
attr(object$x,"smooth") <- list(bw=object$bwVal, w=object$w, kernel=object$kernel)
} else {
object$k1 <- 1
object$k2 <- 1
object$w <- kernel(1)
object$bwVal <- 1
}
object$g <- .momentFct
object$CGEL <- object$alpha
object$typeDesc <- object$type
class(object) <- clname
return(object)
}
getModel.ateGel <- function(object, ...)
{
object$allArg <- c(object, list(...))
object$allArg$weights <- NULL
object$allArg$call <- NULL
if(is(object$g, "formula"))
{
dat <- getDat(object$g, object$x, data = object$data)
if (!is.null(object$w))
if (is(object$w, "formula"))
{
dat$w <- model.matrix(object$w, object$data)[,-1,drop=FALSE]
} else {
stop("w must be a formula")
}
if (dat$ny>1 | dat$ny==0)
stop("You need one and only one dependent variable")
k <- dat$k
if (k>2 & object$momType=="ATT")
stop("Cannot compute ATT with multiple treatments")
if (attr(dat$mt, "intercept")!=1)
stop("An intercept is needed to compute the treatment effect")
if (!all(dat$x[,3:(k+1)] %in% c(0,1)))
stop("The treatment indicators can only take values 0 or 1")
if (colnames(dat$x)[k+2] == "(Intercept)")
{
dat$x <- dat$x[,-(k+2)]
dat$nh <- dat$nh-1
}
if (!is.null(object$popMom))
{
if (length(object$popMom)!=dat$nh)
stop("The dim. of the population moments does not match the dim. of the vector of covariates")
}
if (is.null(object$tet0))
{
if (is.null(dat$w))
{
tet0 <- lm(dat$x[,1]~dat$x[,3:(k+1)])$coef
} else {
tet0 <- lm(dat$x[,1]~dat$x[,3:(k+1)]+dat$w)$coef
}
tet0 <- c(tet0, colMeans(dat$x[,3:(k+1),drop=FALSE]))
names(tet0) <- NULL
object$tet0 <- tet0
} else {
ntet0 <- 2*k-1 + ifelse(is.null(dat$w), 0, ncol(dat$w))
if (length(object$tet0) != ntet0)
stop("tet0 should have a length equal to 2x(number of treatments)+1+number of w's if any")
}
if (object$family != "linear")
{
if (any(!(dat$x[,1]%in%c(0,1))))
stop("For logit or probit, Y can only take 0s and 1s")
family <- binomial(link=object$family)
if (object$family == "logit")
family$mu.eta2 <- function(x, family) family$mu.eta(x)*(1-2*family$linkinv(x))
else
family$mu.eta2 <- function(x, family) -x*family$mu.eta(x)
} else {
family <- NULL
}
q <- dat$nh + 2*k+1
if (object$momType != "bal" | !is.null(object$popMom))
q <- q+dat$nh
if (!is.null(dat$w))
{
q <- q+ncol(dat$w)
namew <- colnames(dat$w)
} else {
namew <- NULL
}
object$gradv <- .DmomentFctATE
object$x <- dat
object$gradvf <- FALSE
object$gform<-object$g
namex <- colnames(dat$x[,2:(k+1)])
nameh <- colnames(dat$x[,(k+2):ncol(dat$x), drop=FALSE])
namesCoef <- c(namex, namew, paste("TreatProb", 1:(k-1), sep=""))
namesgt <- paste(rep(paste("Treat", 1:(k-1), sep=""),
rep(dat$nh, k-1)), "_", rep(nameh, k-1), sep="")
object$namesgt <- c(namesCoef,namesgt)
if (object$momType != "bal" | !is.null(object$popMom))
object$namesgt <- c(object$namesgt, paste("bal_", nameh, sep=""))
if (is.null(names(object$tet0)))
object$namesCoef <- namesCoef
else
object$namesCoef <- names(object$tet0)
attr(object$x,"ModelType") <- "linear"
attr(object$x, "k") <- k
attr(object$x, "q") <- q
attr(object$x, "n") <- nrow(dat$x)
attr(object$x, "momType") <- object$momType
attr(object$x, "popMom") <- object$popMom
attr(object$x, "family") <- family
} else {
stop("Not implemented yet for nonlinear regression")
}
if (object$momType == "ATT")
metname <- "ATT"
else
metname <- "ATE"
if (!is.null(object$popMom))
{
metname2 <- " with balancing based on population moments"
} else {
if (object$momType == "balSample")
metname2 <- " with balancing based on the sample moments"
else
metname2 <- " with unrestricted balancing"
}
metname3 <- paste("\nMethod: ", object$type, sep="")
if (!is.null(family))
metname3 <- paste(metname3, ", Family: Binomial with ", family$link, " link", sep="")
clname <- "baseGel.mod"
object$k1 <- 1
object$k2 <- 1
object$w <- kernel(1)
object$bwVal <- 1
object$g <- .momentFctATE
object$CGEL <- object$alpha
object$typeDesc <- paste(metname, metname2, metname3, sep="")
class(object) <- clname
return(object)
}
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