# summary: Method for object of class gmm or gel In gmm: Generalized Method of Moments and Generalized Empirical Likelihood

## Description

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.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```## 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

## Value

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(θ,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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33``` ```# 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) ```

gmm documentation built on March 18, 2018, 2:30 p.m.