summary.gmm {gmm} | R Documentation |
It presents the results from the gmm
estimation in the same fashion as summary does for the lm class objects for example. It also compute the J-test for overidentifying restriction.
## S3 method for class 'gmm': summary(object, ...)
object |
An object of class gmm returned by the function gmm |
... |
Other arguments when summary is applied to an other classe object |
It returns a list with the parameter estimates and theirs standard deviations, t-stat and p-values. It also returns the J-test and p-value for the null hypothesis that E(g(theta,X)=0
Hansen, L.P. (1982), Large Sample Properties of Generalized Method of Moments Estimators. Econometrica, 50, 1029-1054,
Hansen, L.P. and Heaton, J. and Yaron, A.(1996), Finit-Sample Properties of Some Alternative GMM Estimators. Journal of Business and Economic Statistics, 14 262-280.
n = 500 phi<-c(.2,.7) thet <- 0 sd <- .2 x <- matrix(arima.sim(n=n,list(order=c(2,0,1),ar=phi,ma=thet,sd=sd)),ncol=1) 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[5:(n-2)],u*x[4:(n-3)],u*x[3:(n-4)]) #f <- cbind(u,u*x[5:(n-2)],u*x[4:(n-3)],u*x[3:(n-4)],u*x[2:(n-5)],u*x[1:(n-6)]) #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) } Dg <- function(tet,x) { n <- nrow(x) xx <- cbind(rep(1,(n-6)),x[6:(n-1)],x[5:(n-2)]) H <- cbind(rep(1,(n-6)),x[5:(n-2)],x[4:(n-3)],x[3:(n-4)]) # H <- cbind(rep(1,(n-6)),x[5:(n-2)],x[4:(n-3)],x[3:(n-4)],x[2:(n-5)],x[1:(n-6)]) #H <- cbind(rep(1,(n-5)),x[4:(n-3)],x[3:(n-4)],x[2:(n-5)],x[1:(n-6)]) f <- -crossprod(H,xx)/(n-6) return(f) } resgmm <- gmm(g,c(0,.3,.6),x,grad=Dg) summary(resgmm)