Two stage least squares estimation

It extracts the matrix of variances and covariances from `gmm`

or `gel`

objects.

1 2 3 4 5 6 |

`object` |
An object of class |

`lambda` |
If set to TRUE, the covariance matrix of the Lagrange multipliers is produced. |

`type` |
Type of covariance matrix for the meat |

`hacProp` |
A list of arguments to pass to |

`...` |
Other arguments when |

For tsls(), if vcov is set to a different value thand "Classical", a sandwich covariance matrix is computed.

A matrix of variances and covariances

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
# GMM #
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)]
H <- cbind(x[4:(n-3)], x[3:(n-4)], x[2:(n-5)], x[1:(n-6)])
g <- y ~ ym1 + ym2
x <- H
res <- gmm(g, x)
vcov(res)
## GEL ##
t0 <- c(0,.5,.5)
res <- gel(g, x, t0)
vcov(res)
vcov(res, lambda = TRUE)
``` |

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