sampleSelection: tobit2fit – R documentation

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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.

Author(s)

Ott Toomet otoomet@ut.ee, Arne Henningsen

References

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