# tsls: Two stage least squares estimation In gmm: Generalized Method of Moments and Generalized Empirical Likelihood

## Description

Function to estimate a linear model by the two stage least squares method.

## Usage

 1 tsls(g,x,data) 

## Arguments

 g A formula describing the linear regression model (see details below). x The matrix of instruments (see details below). data A data.frame or a matrix with column names (Optionnal).

## Details

The function just calls gmm with the option vcov="iid". It just simplifies the the implementation of 2SLS. The users don't have to worry about all the options offered in gmm. The model is

Y_i = X_iβ + u_i

In the first step, lm is used to regress X_i on the set of instruments Z_i. The second step also uses lm to regress Y_i on the fitted values of the first step.

## Value

'tsls' returns an object of 'class' '"tsls"' which inherits from class '"gmm"'.

The functions 'summary' is used to obtain and print a summary of the results. It also compute the J-test of overidentying restriction

The object of class "gmm" is a list containing at least:

 coefficients k\times 1 vector of coefficients residuals the residuals, that is response minus fitted values if "g" is a formula. fitted.values the fitted mean values if "g" is a formula. vcov the covariance matrix of the coefficients objective the value of the objective function \| var(\bar{g})^{-1/2}\bar{g}\|^2 terms the terms object used when g is a formula. call the matched call. y if requested, the response used (if "g" is a formula). x if requested, the model matrix used if "g" is a formula or the data if "g" is a function. model if requested (the default), the model frame used if "g" is a formula. algoInfo Information produced by either optim or nlminb related to the convergence if "g" is a function. It is printed by the summary.gmm method.

## References

Hansen, L.P. (1982), Large Sample Properties of Generalized Method of Moments Estimators. Econometrica, 50, 1029-1054,

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 n <- 1000 e <- arima.sim(n,model=list(ma=.9)) C <- runif(n,0,5) Y <- rep(0,n) Y[1] = 1 + 2*C[1] + e[1] for (i in 2:n){ Y[i] = 1 + 2*C[i] + 0.9*Y[i-1] + e[i] } Yt <- Y[5:n] X <- cbind(C[5:n],Y[4:(n-1)]) Z <- cbind(C[5:n],Y[3:(n-2)],Y[2:(n-3)],Y[1:(n-4)]) res <- tsls(Yt~X,~Z) res 

gmm documentation built on June 20, 2017, 3:01 p.m.