It computes the vector of Lagrange multipliers, which maximizes the GEL objective function, using an iterative Newton method.

1 2 |

`gt` |
A |

`l0` |
Vector of starting values for lambda |

`type` |
"EL" for empirical likelihood, "ET" for exponential tilting and "CUE" for continuous updated estimator. See details for "ETEL". |

`tol_lam` |
Tolerance for |

`maxiterlam` |
The algorithm stops if there is no convergence after "maxiterlam" iterations. |

`tol_obj` |
Tolerance for the gradiant of the objective function. The algorithm returns a non-convergence message if |

`k` |
It represents the ratio k1/k2, where |

`method` |
The iterative procedure uses a Newton method for solving the FOC. It i however recommended to use |

`control` |
Controls to send to |

It solves the problem *\max_{λ} \frac{1}{n}∑_{t=1}^n ρ(gt'λ)*. For the type "ETEL", it is only used by `gel`

. In that case *λ* is obtained by maximizing *\frac{1}{n}∑_{t=1}^n ρ(gt'λ)*, using *ρ(v)=-\exp{v}* (so ET) and *θ* by minimizing the same equation but with *ρ(v)-\log{(1-v)}*. To avoid NA's, `constrOptim`

is used with the restriction *λ'g_t < 1*.

lambda: A *q\times 1* vector of Lagrange multipliers which solve the system of equations given above.
`conv`

: Details on the type of convergence.

Newey, W.K. and Smith, R.J. (2004), Higher Order Properties of GMM and
Generalized Empirical Likelihood Estimators. *Econometrica*, **72**, 219-255.

Smith, R.J. (2004), GEL Criteria for Moment Condition Models. *Working paper, CEMMAP*.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
g <- function(tet,x)
{
n <- nrow(x)
u <- (x[7:n] - tet[1] - tet[2]*x[6:(n-1)] - tet[3]*x[5:(n-2)])
f <- cbind(u, u*x[4:(n-3)], u*x[3:(n-4)], u*x[2:(n-5)], u*x[1:(n-6)])
return(f)
}
n = 500
phi<-c(.2, .7)
thet <- 0.2
sd <- .2
x <- matrix(arima.sim(n = n, list(order = c(2, 0, 1), ar = phi, ma = thet, sd = sd)), ncol = 1)
gt <- g(c(0,phi),x)
getLamb(gt, type = "EL",method="optim")
``` |

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