These functions do the actual fitting of tobit-2
(sample selection) and tobit-5 (switching regression)
models by Maximum Likelihood (ML) estimation.
The arguments must be given as numeric vectors/matrices,
initial value of parameters must be specified.
These functions are called by selection and
are intended for sampleSelection internal use.
The function tobit2Bfit does the actual fitting of tobit-2
(sample selection) models with a binary dependent variable
of the outcome model (YO) using a double-probit specification.
tobit2fit( YS, XS, YO, XO, start, weights = NULL, print.level = 0,
maxMethod = "Newton-Raphson", ... )tobit2Bfit( YS, XS, YO, XO, start, weights = NULL, print.level = 0,
maxMethod = "BHHH", ... )
tobit5fit( YS, XS, YO1, XO1, YO2, XO2, start, print.level = 0,
maxMethod = "Newton-Raphson", ... )
numeric 0/1 vector, where 0 denotes unobserved outcome (tobit 2) or outcome 1 observed (tobit 5).
numeric matrix, model matrix for selection and outcome equations.
numeric vector, observed outcomes. Values for unobserved outcomes are ignored (they may or may not be NA).
numeric vector of initial values. The order is: betaS, betaO(1), sigma(1), rho(1), betaO2, sigma2, rho2.
an optional vector of ‘prior weights’ to be used in the fitting process. Should be NULL or a numeric vector. Weights are currently only supported in type-2 models.
numeric, values greater than 0 will produce increasingly more debugging information.
character, a maximisation method supported by maxLik
Additional parameters to maxLik.
Object of class "selection". It inherits from class "maxLik" and
includes two additional components: $tobitType, numeric
tobit model classifier (see Amemiya, 1985), and $method, either "ml"
or "2step", specifying the estimation method.
Amemiya, T. (1985) Advanced Econometrics, Harvard University Press