R: Pseudo conditional maximum likelihood estimation of the...
cquad_pseudo
R Documentation
Pseudo conditional maximum likelihood estimation of the dynamic logit model
Description
Estimate the dynamic logit model for binary logitudinal data by the pseudo conditional maximum likelihood method proposed by Bartolucci & Nigro (2012).
Usage
cquad_pseudo(id, yv, X = NULL, be = NULL)
Arguments
id
list of the reference unit of each observation
yv
corresponding vector of response variables
X
corresponding matrix of covariates (optional)
be
intial vector of parameters (optional)
Value
formula
formula defining the model
lk
conditional log-likelihood value
coefficients
estimate of the regression parameters (including for the lag-response)
vcov
asymptotic variance-covariance matrix for the parameter estimates
scv
matrix of individual scores
J
Hessian of the log-likelihood function
se
standard errors
se2
robust standard errors that also take into account the first step
Tv
number of time occasions for each unit
Author(s)
Francesco Bartolucci (University of Perugia), Claudia Pigini (University of Ancona "Politecnica delle Marche")
References
Bartolucci, F. and Nigro, V. (2010), A dynamic model for binary panel data with unobserved heterogeneity admitting a root-n consistent conditional estimator, Econometrica, 78, 719-733.
Bartolucci, F. and Nigro, V. (2012), Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data, Journal of Econometrics, 170, 102-116.
Examples
## Not run:
# example based on simulated data
data(data_sim)
data_sim = data_sim[1:500,] # to speed up the example, remove otherwise
id = data_sim$id; yv = data_sim$y; X = cbind(X1=data_sim$X1,X2=data_sim$X2)
# estimate dynmic logit model
out = cquad_pseudo(id,yv,X)
summary(out)
## End(Not run)