tsls: Two stage least squares estimation

In gmm: Generalized Method of Moments and Generalized Empirical Likelihood

Two stage least squares estimation

Description

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

Usage

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_i\beta + 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

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 7, 2023, 6:05 p.m.