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Ajout de la fonction de GMM itératif

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François Pelletier 2014-03-02 20:20:34 -05:00
parent 6ee762daea
commit 79246138f2
8 changed files with 101 additions and 20 deletions
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9
R/covariance.GMM.R
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@ -7,12 +7,13 @@
#' Estimated covariance matrix
#' @param conditions.vector Vector of moment conditions
#' @param n Sample size
#' @param ... Parameters of the vector of moment conditions
#' @param param Vector of estimated parameters
#' @param sample Individual data sample
#' @param ... Functions of the vector of moment conditions
#' @return A square covariance matrix
#'
#' @author François Pelletier
covariance.GMM <- function(conditions.vector,n,...)
covariance.GMM <- function(conditions.vector,param,sample...)
{
t(conditions.vector(...)) %*% conditions.vector(...) / n
t(conditions.vector(param,sample,...)) %*% conditions.vector(param,sample,...) / length(sample)
}

39
R/iterative.GMM.R Normal file
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@ -0,0 +1,39 @@
# TODO: Add comment
#
# Author: francois
###############################################################################
#' @param start Starting values for the parameters and lagrangian
#' @param conditions.vector Vector of moment conditions
#' @param sample 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
#' @param max.iter Maximum number of iterations
#' @param epsilon Minimum precision level
#' @return A list containing the optimized vector of parameter and corresponding covariance matrix
#'
#' @author François Pelletier
iterative.GMM <- function(start,conditions.vector,sample,...,
W,R,r,max.iter=50,epsilon=1E-6)
{
theta1 <- optim.GMM(start,conditions.vector,sample,...,W,R,r)
i <- 1
repeat
{
theta2 <- optim.GMM(theta1,conditions.vector,sample,...,W,R,r)
S <- covariance.GMM(conditions.vector,param,sample,...)
if(sqrt(sum((theta1-theta2)^2))<epsilon)
return(list(par=theta2,cov=S))
else if (i>max.iter)
stop("Iterative GMM does not converge")
else
{
theta1 <- theta2
i <- i+1
}
}
}

9
R/objective.GMM.R
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@ -6,19 +6,20 @@
###############################################################################
#' Objective function for the GMM method
#' @param param Vector of parameters to optimize
#' @param param.lagrangian Vector of parameters and Lagrangian to optimize
#' @param conditions.vector Vector of moment conditions
#' @param ... Parameters of the vector of moment conditions
#' @param sample 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
#'
#' @author François Pelletier
objective.GMM <- function(param.lagrangian,conditions.vector,num.param,...,
objective.GMM <- function(param.lagrangian,conditions.vector,sample,...,
W=diag(length(conditions.vector)),R=0,r=0)
{
param <- param.lagrangian[1:num.param]
lagrangian <- param.lagrangian[num.param+1:length(param.lagrangian)]
colMeans(conditions.vector(param,...)) %*% ginv(W) %*% colMeans(conditions.vector(param,...))+ abs(t(R %*% param - r) %*% lagrangian)
colMeans(conditions.vector(param,sample,...)) %*% ginv(W) %*% colMeans(conditions.vector(param,sample,...))+ abs(t(R %*% param - r) %*% lagrangian)
}

9
R/optim.GMM.R
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@ -2,14 +2,15 @@
#'
#' @param start Starting values for the parameters and lagrangian
#' @param conditions.vector Vector of moment conditions
#' @param number of parameters of the distribution
#' @param ... Parameters of the vector of moment conditions
#' @param sample 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
#'
#' @author François Pelletier
optim.GMM <- function(start,conditions.vector,num.param,...,W,R,r)
optim.GMM <- function(start,conditions.vector,sample,...,W,R,r)
{
optim(c(start,objective.GMM,conditions.vector,num.param,...,W,R,r))
optim(start,objective.GMM,conditions.vector,sample,...,W,R,r)
}