Fitting Parametric Sample Selection Models
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", ... )
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.
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
Ott Toomet otoomet@ut.ee , Arne Henningsen
selection, maxLik