summary.gel {gmm} | R Documentation |
It presents the results from the gel
estimation in the same fashion as summary does for the lm class objects for example. It also compute the J-test, LM and LR tests for overidentifying restriction.
## S3 method for class 'gel': summary(object, interval=FALSE, ...)
object |
An object of class gmm returned by the function gmm |
interval |
Should the results include the confidence intervals of hat{theta} and hat{λ}. If so, "interval" should be equal to the confidence level. |
... |
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 three tests (J, LM and LR) and p-value for the null hypothesis that E(g(theta,X)=0
Anatolyev, S. (2005), GMM, GEL, Serial Correlation, and Asymptotic Bias. Econometrica, 73, 983-1002.
Kitamura, Yuichi (1997), Empirical Likelihood Methods With Weakly Dependent Processes. The Annals of Statistics, 25, 2084-2102.
Newey, W.K. and Smith, R.J. (2004), Higher Order Properties of GMM and Generalized Empirical Likelihood Estimators. Econometrica, 72, 219-255.
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) y <- x[7:n] ym1 <- x[6:(n-1)] ym2 <- x[5:(n-2)] H <- cbind(x[4:(n-3)],x[3:(n-4)],x[2:(n-5)],x[1:(n-6)]) g <- y~ym1+ym2 x <- H t0 <- c(0,.5,.5) res <- gel(g,x,t0) summary(res) summary(res,interval=0.95)