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sampleSelection (version 1.0-4)

tobit2fit: Fitting Parametric Sample Selection Models

Description

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.

Usage

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", ... )

Arguments

YS
numeric 0/1 vector, where 0 denotes unobserved outcome (tobit 2) or outcome 1 observed (tobit 5).
XS, XO, XO1, XO2
numeric matrix, model matrix for selection and outcome equations.
YO
numeric vector, observed outcomes. Values for unobserved outcomes are ignored (they may or may not be NA).
start
numeric vector of initial values. The order is: betaS, betaO(1), sigma(1), rho(1), betaO2, sigma2, rho2.
weights
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.
print.level
numeric, values greater than 0 will produce increasingly more debugging information.
maxMethod
character, a maximisation method supported by maxLik
...
Additional parameters to maxLik.

Value

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.

References

Amemiya, T. (1985) Advanced Econometrics, Harvard University Press

See Also

selection, maxLik