HeckmanSK {ssmodels} | R Documentation |
Skew-Normal Sample Selection Model Fit Function
Description
Fits a sample selection model based on the Skew-Normal distribution using Maximum Likelihood Estimation (MLE). This model allows for asymmetry in the distribution of the outcome variable's error term, addressing potential skewness.
Usage
HeckmanSK(
selection,
outcome,
data = sys.frame(sys.parent()),
lambda,
start = NULL
)
Arguments
selection |
A formula specifying the selection equation. |
outcome |
A formula specifying the outcome equation. |
data |
A data frame containing the variables. |
lambda |
Initial start value for the skewness parameter ( |
start |
Optional numeric vector of initial parameter values. |
Details
The function implements MLE for a sample selection model where the outcome equation's errors follow a Skew-Normal distribution, as proposed in Ogundimu and Hutton (2016). The optimization is performed via the BFGS algorithm.
The results include estimates for:
Selection equation coefficients.
Outcome equation coefficients.
Standard deviation of the error term (
sigma
).Correlation between the selection and outcome errors (
rho
).Skewness parameter (
lambda
).Robust standard errors from the Fisher information matrix.
Value
A list containing:
-
coefficients
: Named vector of estimated model parameters. -
value
: The (negative) log-likelihood at convergence. -
loglik
: The maximum log-likelihood. -
counts
: Number of gradient evaluations. -
hessian
: Hessian matrix at the optimum. -
fisher_infoSK
: Approximate Fisher information matrix. -
prop_sigmaSK
: Standard errors for the estimates. -
level
: Levels of the selection variable. -
nObs
: Number of observations. -
nParam
: Number of model parameters. -
N0
: Number of censored (unobserved) observations. -
N1
: Number of observed (uncensored) observations. -
NXS
: Number of covariates in the selection equation. -
NXO
: Number of covariates in the outcome equation. -
df
: Degrees of freedom (observations minus parameters). -
aic
: Akaike Information Criterion. -
bic
: Bayesian Information Criterion. -
initial.value
: Initial parameter values used.
References
Emmanuel O Ogundimu, Jane L Hutton (2016). “A Sample Selection Model with Skew-normal Distribution.” Scandinavian Journal of Statistics, 43(1), 172–190.
Examples
data("Mroz87")
attach(Mroz87)
selectEq <- lfp ~ huswage + kids5 + mtr + fatheduc + educ + city
outcomeEq <- log(wage) ~ educ + city
HeckmanSK(selectEq, outcomeEq, data = Mroz87, lambda = -1.5)