gmnl (version 1.1-4)

cov.gmnl: Functions for Correlated Random Parameters

Description

These are a set of functions that help to extract the variance-covariance matrix, the correlation matrix, and the standard error of the random parameters for models of class gmnl.

Usage

cov.gmnl(x, Q = NULL)
cor.gmnl(x, Q = NULL)
se.cov.gmnl(x, sd = FALSE, Q = NULL, digits = max(3, getOption("digits") - 2))

Value

cov.gmnl returns a matrix with the variance of the random parameters if the model is fitted with random coefficients. If the model is fitted with correlation = TRUE, then the variance-covariance matrix is returned.

If correlation = TRUE in the fitted model, then se.cov.gmnl returns a coefficient matrix for the elements of the variance-covariance matrix or the standard deviations if sd = TRUE.

Arguments

x

an object of class gmnl where ranp is not NULL.

Q

this argument is only valid if the "mm" (MM-MNL) model is estimated. It indicates the class for which the variance-covariance matrix is computed.

sd

if TRUE, then the standard deviations of the random parameters along with their standard errors are computed.

digits

the number of digits.

Author

Mauricio Sarrias

Details

The variance-covariance matrix is computed using the Cholesky decomposition \(LL'=\Sigma\).

se.cov.gmnl function is a wrapper for the deltamethod function of the msm package.

References

• Greene, W. H. (2012). Econometric Analysis, Seventh Edition. Pearson Hall.

• Train, K. (2009). Discrete Choice Methods with Simulation. Cambridge University Press.

See Also

gmnl for the estimation of different multinomial models with individual heterogeneity.

Examples

if (FALSE) {
## Examples using Electricity data set from mlogit package
library(mlogit)
data("Electricity", package = "mlogit")
Electr <- mlogit.data(Electricity, id.var = "id", choice = "choice",
                     varying = 3:26, shape = "wide", sep = "")
                     
## Estimate a MIXL model with correlated random parameters
Elec.cor <- gmnl(choice ~ pf + cl + loc + wk + tod + seas| 0, data = Electr,
                 subset = 1:3000,
                 model = 'mixl',
                 R = 10,
                 panel = TRUE,
                 ranp = c(cl = "n", loc = "n", wk = "n", tod = "n", seas = "n"),
                 correlation = TRUE)
                 
## Use functions for correlated random parameters
cov.gmnl(Elec.cor)
se.cov.gmnl(Elec.cor)
se.cov.gmnl(Elec.cor, sd = TRUE)
cor.gmnl(Elec.cor)
}