# Constructor for `"sysMomentModel"` classes It builds the object of either class `"slinearModel"` or `"snonlinearModel"`, which are system of equations based on moment conditions. ```r sysMomentModel(g, h=NULL, theta0=NULL, grad=NULL, vcov = c("iid", "HAC", "MDS", "CL"), vcovOptions=list(), centeredVcov = TRUE, data=parent.frame(),na.action="na.omit", survOptions=list()) ``` ## Arguments - `g`: A list of linear or nonlinear regression formulas for each equation in the system. - `h`: A list of linear formulas for the instruments in each equation in the system. - `theta0`: A list of vectors of starting values. It is required only when the equations are nonlinear, in which case, it must be a list of named vector, with the names corresponding to the coefficient names in the regression formulas. - `grad`: A list of functions that returns the derivative of the moment functions. Only used if `g` is a list of functions. - `vcov`: Assumption on the properties of the moment conditions. By default, they are weakly dependant processes. For `MDS`, we assume that the conditions are martingale difference sequences, which implies they are serially uncorrelated, but may be heteroscedastic. There is a difference between `iid` and `MDS` only when `g` is a formula. In that case, residuals are assumed homoscedastic as well as serially uncorrelated. For type `CL`, clustered covariance matrix is computed. The options are then included in `vcovOptions` (see `meatCL`). - `vcovOptions`: A list of options for the covariance matrix of the moment conditions. See `vcovHAC` for the default values. - `centeredVcov`: Should the moment function be centered when computing its covariance matrix. Doing so may improve inference. - `data`: A data.frame or a matrix with column names (Optional). - `na.action`: Action to take for missing values. If missing values are present and the option is set to `"na.pass"`, the model won't be estimable. - `survOptions`: If needed, a list with the type of survey weights and the weights as a numeric vector, data.frame or formula. The type is either `"sampling"` or `"fequency"`. ## Returns 'sysMomentModel' returns an object of one of the subclasses of `"sysMomentModel"`. ## References Hayashi, F. (2000). **Econometrics**, New Jersey: Princeton University Press. Andrews DWK (1991), Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation. **Econometrica**, 59 , 817--858. Newey WK & West KD (1987), A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. **Econometrica**, 55 , 703--708. Newey WK & West KD (1994), Automatic Lag Selection in Covariance Matrix Estimation. **Review of Economic Studies**, 61 , 631-653. ## Examples ```r set.seed(1122) x1 <- rchisq(50,5) x2 <- rchisq(50,5) x3 <- rnorm(50) x4 <- rnorm(50) z1 <- .2*x1+rnorm(50) z2 <- .2*x2+rnorm(50) z3 <- rnorm(50) z4 <- rnorm(50) z5 <- rnorm(50) y1 <- x1+rnorm(50) y2 <- 2*x1+rnorm(50) y3 <- 0.5*x2+rnorm(50) dat <- data.frame(y1=y1,y3=y3,y2=y2, z1=z1,x1=x1,z2=z2,x2=x2,z3=z3,x3=x3, x4=x4,z4=z4,z5=z5) g1 <- y1~x1+x4; h1 <- ~z1+z2+z3+z4+x4 g2 <- y2~x1+x2+x3; h2 <- ~z1+z2+z3+z4+x3 g3 <- y3~x2+x3+x4; h3 <- ~z2+z3+z4+x3+x4 g <- list(g1,g2,g3) h <- list(h1,h2,h3) smodel <- sysMomentModel(g, h, data=dat) ## not really nonlinear nlg <- list(y1~theta0+theta1*x1+theta2*x4, y2~alpha0+alpha1*x1+alpha2*x2+alpha3*x3, y3~beta0+beta1*x2+beta2*x3+beta3*x4) theta0 <- list(c(theta0=1,theta1=2,theta2=3), c(alpha0=1,alpha1=2,alpha2=3, alpha3=4), c(beta0=1,beta1=2,beta2=3,beta3=4)) snmodel <- sysMomentModel(nlg, h, theta0, data=dat) ```

sysMomentModel function