get_lamb {gmm} | R Documentation |
It computes the vector of Lagrange multipliers, which maximizes the GEL objective function, using an iterative Newton method.
get_lamb(g,tet,x,type=c('EL','ET','CUE'),tol_lam=1e-12,maxiterlam=1000,tol_obj=1e-7)
g |
A function of the form g(theta,x) and which returns a n times q matrix with typical element g_i(theta,x_t) for i=1,...q and t=1,...,n. This matrix is then used to build the q sample moment conditions. |
tet |
A k times 1 vector of parameters at which the function g(theta,x) has to be evaluated |
x |
The matrix or vector of data from which the function g(theta,x) is computed. |
type |
"EL" for empirical likelihood, "ET" for exponential tilting and "CUE" for continuous updated estimator. |
tol_lam |
Tolerance for λ between two iterations. The algorithm stops when |λ_i -λ_{i-1}| reaches tol_lam |
maxiterlam |
The algorithm stops if there is no convergence after "maxiterlam" iterations. |
tol_obj |
Tolerance for the gradiant of the objective function. The algorithm returns a non-convergence message if max(|gradiant|) does not reach tol_obj . It helps the gel algorithm to select the right space to look for theta |
It solves the problem frac{1}{n}sum_{t=1}^n Drho(<g(theta,x_t),λ>)g(theta,x_t)=0.
lambda: A qtimes 1 vector of Lagrange multipliers which solve the system of equations given above.
singular: 0 for a normal solution, 1 if the algorithm does not converge and 2 if the algorithm produces a singular system, NaN or Inf values.
conv_mes
: A message with details about the convergence.
Newey, W.K. and Smith, R.J. (2004), Higher Order Properties of GMM and Generalized Empirical Likelihood Estimators. Econometrica, 72, 219-255.
g <- function(tet,x) { n <- nrow(x) u <- (x[7:n] - tet[1] - tet[2]*x[6:(n-1)] - tet[3]*x[5:(n-2)]) f <- cbind(u,u*x[4:(n-3)],u*x[3:(n-4)],u*x[2:(n-5)],u*x[1:(n-6)]) return(f) } n = 500 phi<-c(.2,.7) thet <- 0.2 sd <- .2 x <- matrix(arima.sim(n=n,list(order=c(2,0,1),ar=phi,ma=thet,sd=sd)),ncol=1) get_lamb(g,c(0,phi),x,type="EL")