heckitVcov: Heckit Variance Covariance Matrix

In sampleSelection2: Sample Selection Models

Description

Calculate the asymptotic covariance matrix for the coefficients of a Heckit estimation

Usage

   heckitVcov( xMat, wMat, vcovProbit, rho, delta, sigma,
   saveMemory = TRUE )

Arguments

xMat	model matrix of the 2nd step estimation.
wMat	model matrix of the 1st step probit estimation.
vcovProbit	variance covariance matrix of the 1st step probit estimation.
rho	the estimated ρ, see Greene (2003, p. 784).
delta	the estimated δs, see Greene (2003, p. 784).
sigma	the estimated σ, see Greene (2003, p. 784).
saveMemory	logical. Save memory by using a different implementation of the formula? (this should not influence the results).

Details

The formula implemented in heckitVcov is available, e.g., in Greene (2003), last formula on page 785.

Value

the variance covariance matrix of the coefficients.

Author(s)

Arne Henningsen

References

Greene, W. H. (2003) Econometric Analysis, Fifth Edition, Prentice Hall.

Lee, L., G. Maddala and R. Trost (1980) Asymetric covariance matrices of two-stage probit and two-stage tobit methods for simultaneous equations models with selectivity. Econometrica, 48, p. 491-503.

See Also

heckit.

sampleSelection2 documentation built on May 2, 2019, 6:08 p.m.