summary function

Method for object of class gmm or gel

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 'sysGmm'
summary(object, ...)
## S3 method for class 'gel'
summary(object, ...)
## S3 method for class 'ategel'
summary(object, robToMiss = TRUE, ...)
## S3 method for class 'tsls'
summary(object, vcov = NULL, ...)
## S3 method for class 'summary.gmm'
print(x, digits = 5, ...)
## S3 method for class 'summary.sysGmm'
print(x, digits = 5, ...)
## S3 method for class 'summary.gel'
print(x, digits = 5, ...)
## S3 method for class 'summary.tsls'
print(x, digits = 5, ...)

Arguments

• 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
• vcov: An alternative covariance matrix computed with vcov.tsls
• robToMiss: If TRUE, it computes the robust to misspecification covariance matrix
• ...: Other arguments when summary is applied to another class object

Returns

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 $E(g(\theta,X)=0$

References

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.

Examples

# GMM #
set.seed(444)
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)]
ym3 <- x[4:(n-3)]
ym4 <- x[3:(n-4)]
ym5 <- x[2:(n-5)]
ym6 <- x[1:(n-6)]

g <- y ~ ym1 + ym2
x <- ~ym3+ym4+ym5+ym6

res <- gmm(g, x)

summary(res)

# GEL #

t0 <- res$coef
res <- gel(g, x, t0)
summary(res)

# tsls #

res <- tsls(y ~ ym1 + ym2,~ym3+ym4+ym5+ym6)
summary(res)