# 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. ```r ## 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 ```r # 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) ```

summary function