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enlever la partie contraintes, va refaire d'autres fonctions plus tard

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pour ça
This commit is contained in:
François Pelletier 2014-03-08 00:37:30 -05:00
parent ca602c5f75
commit 445e334b28
8 changed files with 40 additions and 49 deletions
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12
R/iterative.GMM.R
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@ -9,7 +9,7 @@
#'
#' @param start Starting values for the parameters and lagrangian
#' @param conditions.vector Vector of moment conditions
#' @param sample Individual data sample
#' @param data Individual data sample
#' @param ... Functions of the vector of moment conditions
#' @param W Weighting matrix
#' @param R Linear constraint matrix of coefficients
@ -19,15 +19,15 @@
#' @return A list containing the optimized vector of parameter and corresponding covariance matrix
#' @export iterative.GMM
#' @author François Pelletier
iterative.GMM <- function(start,conditions.vector,sample,...,
W,R,r,max.iter=50,epsilon=1E-6)
iterative.GMM <- function(start,conditions.vector,data,...,
W=diag(length(conditions.vector)),R=0,r=0,lagrangian.start=rep(0,length(conditions.vector)),max.iter=50,epsilon=1E-6)
{
theta1 <- optim.GMM(start,conditions.vector,sample,...,W,R,r)
theta1 <- optim.GMM(start,conditions.vector,data,...,W,R,r,lagrangian.start)
i <- 1
repeat
{
theta2 <- optim.GMM(theta1,conditions.vector,sample,...,W,R,r)
S <- covariance.GMM(conditions.vector,param,sample,...)
theta2 <- optim.GMM(theta1,conditions.vector,data,...,W,R,r,lagrangian.start)
S <- covariance.GMM(conditions.vector,param,data,...)
if(sqrt(sum((theta1-theta2)^2))<epsilon)
return(list(par=theta2,cov=S))
else if (i>max.iter)

4
R/mean.variance.gmm.vector.R
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@ -8,13 +8,13 @@
#' GMM vector for mean and variance moment conditions
#' @param param Estimated parameters
#' @param sample Data Sample
#' @param X Data Sample
#' @param meanf Mean function
#' @param variancef Variance function
#' @return A two column matrix of differences
#' @export meanvariance.gmm.vector
#' @author François Pelletier
meanvariance.gmm.vector <- function(param,sample,meanf,variancef)
meanvariance.gmm.vector <- function(param,X,meanf,variancef)
{
cbind(X-meanf(param),(X-meanf(param))^2 - variancef(param))
}

13
R/objective.GMM.R
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@ -6,20 +6,15 @@
###############################################################################
#' Objective function for the GMM method
#' @param param.lagrangian Vector of parameters and Lagrangian to optimize
#' @param param Vector of parameters
#' @param conditions.vector Vector of moment conditions
#' @param sample Individual data sample
#' @param data Individual data sample
#' @param ... Functions of the vector of moment conditions
#' @param W Weighting matrix
#' @param R Linear constraint matrix of coefficients
#' @param r Linear constraint constants
#' @return A scalar value
#' @export objective.GMM
#' @author François Pelletier
objective.GMM <- function(param.lagrangian,conditions.vector,sample,...,
W=diag(length(conditions.vector)),R=0,r=0)
objective.GMM <- function(param,conditions.vector,data,W,...)
{
param <- param.lagrangian[1:num.param]
lagrangian <- param.lagrangian[num.param+1:length(param.lagrangian)]
colMeans(conditions.vector(param,sample,...)) %*% ginv(W) %*% colMeans(conditions.vector(param,sample,...))+ abs(t(R %*% param - r) %*% lagrangian)
as.vector(colMeans(conditions.vector(param,data,...)) %*% ginv(W) %*% colMeans(conditions.vector(param,data,...)))
}

21
R/optim.GMM.R
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@ -1,16 +1,21 @@
#' Optimization with constraints for GMM methos
# Optimization for GMM method
#
# Author: Francois Pelletier
#
# LGPL-3.0
###############################################################################
#' Optimization for GMM method
#'
#' @param start Starting values for the parameters and lagrangian
#' @param start Starting values for the parameters
#' @param conditions.vector Vector of moment conditions
#' @param sample Individual data sample
#' @param data Individual data sample
#' @param ... Functions of the vector of moment conditions
#' @param W Weighting matrix
#' @param R Linear constraint matrix of coefficients
#' @param r Linear constraint constants
#' @return une liste contenant le résultat de l'optimisation
#' @return a list with optimization results
#' @export optim.GMM
#' @author François Pelletier
optim.GMM <- function(start,conditions.vector,sample,...,W,R,r)
optim.GMM <- function(start,conditions.vector,data,W,...)
{
optim(start,objective.GMM,conditions.vector,sample,...,W,R,r)
optim(start,objective.GMM,conditions.vector,data,W,...)
}

6
man/iterative.GMM.Rd
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@ -2,7 +2,9 @@
\alias{iterative.GMM}
\title{Iterative GMM method}
\usage{
iterative.GMM(start, conditions.vector, sample, ..., W, R, r, max.iter = 50,
iterative.GMM(start, conditions.vector, data, ...,
W = diag(length(conditions.vector)), R = 0, r = 0,
lagrangian.start = rep(0, length(conditions.vector)), max.iter = 50,
epsilon = 1e-06)
}
\arguments{
@ -11,7 +13,7 @@ iterative.GMM(start, conditions.vector, sample, ..., W, R, r, max.iter = 50,
\item{conditions.vector}{Vector of moment conditions}
\item{sample}{Individual data sample}
\item{data}{Individual data sample}
\item{...}{Functions of the vector of moment conditions}

4
man/meanvariance.gmm.vector.Rd
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@ -2,12 +2,12 @@
\alias{meanvariance.gmm.vector}
\title{GMM vector for mean and variance moment conditions}
\usage{
meanvariance.gmm.vector(param, sample, meanf, variancef)
meanvariance.gmm.vector(param, X, meanf, variancef)
}
\arguments{
\item{param}{Estimated parameters}
\item{sample}{Data Sample}
\item{X}{Data Sample}
\item{meanf}{Mean function}

12
man/objective.GMM.Rd
View file

@ -2,24 +2,18 @@
\alias{objective.GMM}
\title{Objective function for the GMM method}
\usage{
objective.GMM(param.lagrangian, conditions.vector, sample, ...,
W = diag(length(conditions.vector)), R = 0, r = 0)
objective.GMM(param, conditions.vector, data, W, ...)
}
\arguments{
\item{param.lagrangian}{Vector of parameters and
Lagrangian to optimize}
\item{param}{Vector of parameters}
\item{conditions.vector}{Vector of moment conditions}
\item{sample}{Individual data sample}
\item{data}{Individual data sample}
\item{...}{Functions of the vector of moment conditions}
\item{W}{Weighting matrix}
\item{R}{Linear constraint matrix of coefficients}
\item{r}{Linear constraint constants}
}
\value{
A scalar value

17
man/optim.GMM.Rd
View file

@ -1,30 +1,25 @@
\name{optim.GMM}
\alias{optim.GMM}
\title{Optimization with constraints for GMM methos}
\title{Optimization for GMM method}
\usage{
optim.GMM(start, conditions.vector, sample, ..., W, R, r)
optim.GMM(start, conditions.vector, data, W, ...)
}
\arguments{
\item{start}{Starting values for the parameters and
lagrangian}
\item{start}{Starting values for the parameters}
\item{conditions.vector}{Vector of moment conditions}
\item{sample}{Individual data sample}
\item{data}{Individual data sample}
\item{...}{Functions of the vector of moment conditions}
\item{W}{Weighting matrix}
\item{R}{Linear constraint matrix of coefficients}
\item{r}{Linear constraint constants}
}
\value{
une liste contenant le résultat de l'optimisation
a list with optimization results
}
\description{
Optimization with constraints for GMM methos
Optimization for GMM method
}
\author{
François Pelletier