logit function

Estimation of a logit model

Maximum Likelihood (ML) estimation of a logit model.

logit(y, x, initial.values = NULL, lower = -Inf, upper = Inf, 
    method = 2, lag.length = NULL, control = list(), eps.tol = .Machine$double.eps, 
    solve.tol = .Machine$double.eps )

Arguments

• y: numeric vector, the binary process
• x: numeric matrix, the regressors
• initial.values: NULL or a numeric vector with the initial parameter values passed on to the optimisation routine, nlminb. If NULL, the default, then the values are chosen automatically
• lower: numeric vector, either of length 1 or the number of parameters to be estimated, see nlminb
• upper: numeric vector, either of length 1 or the number of parameters to be estimated, see nlminb
• method: an integer that determines the expression for the coefficient-covariance, see "details"
• lag.length: NULL or an integer that determines the lag-length used in the robust coefficient covariance. If lag.length is an integer, then it is ignored unless method = 3
• control: a list passed on to the control argument of nlminb
• eps.tol: numeric, a small value that ensures the fitted zero-probabilities are not too small when the log-transformation is applied when computing the log-likelihood
• solve.tol: numeric value passed on to the tol argument of solve, which is called whenever the coefficient-coariance matrix is computed. The value controls the toleranse for detecting linear dependence between columns when inverting a matrix

Details

No details for the moment.

Returns

A list.

References

No references for the moment.

Author(s)

Genaro Sucarrat, http://www.sucarrat.net/

See Also

nlminb, solve

Examples

##no examples for the moment