summary {gmm} | R Documentation |
It presents the results from the gmm
or gel
estimation in the same fashion as summary
does for the lm
class objects for example. It also compute the tests for overidentifying restrictions.
## S3 method for class 'gmm': summary(object, ...) ## S3 method for class 'gel': summary(object, ...) ## S3 method for class 'summary.gmm': print(x, digits = 5, ...) ## S3 method for class 'summary.gel': print(x, digits = 5, ...)
object |
An object of class gmm or gel returned by the function gmm or gel |
x |
An object of class summary.gmm or summary.gel returned by the function summary.gmm summary.gel |
digits |
The number of digits to be printed |
... |
Other arguments when summary is applied to another class object |
It returns a list with the parameter estimates and their 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.
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.
# GMM # 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 res <- gmm(g, x) summary(res) # GEL # t0 <- c(0,.5,.5) res <- gel(g, x, t0) summary(res)