heckitVcov function

Heckit Variance Covariance Matrix

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

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 $\rho$, see Greene (2003, p. 784).
• delta: the estimated $\delta$s, see Greene (2003, p. 784).
• sigma: the estimated $\sigma$, 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.

Returns

the variance covariance matrix of the coefficients.

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.

Author(s)

Arne Henningsen