cquad (version 2.3)

cquad_pseudo: Pseudo conditional maximum likelihood estimation of the dynamic logit model

Description

Estimate the dynamic logit model for binary longitudinal data by the pseudo conditional maximum likelihood method proposed by Bartolucci & Nigro (2012).

Usage

cquad_pseudo(id, yv, X = NULL, be = NULL, w = rep(1,n), Ttol=10)

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

Arguments

id

list of the reference unit of each observation

yv

corresponding vector of response variables

X

corresponding matrix of covariates (optional)

be

initial vector of parameters (optional)

w

vector of weights (optional)

Ttol

Threshold individual observations that activates the recursive algorithm (default=10)

Author

Francesco Bartolucci (University of Perugia), Claudia Pigini (University of Ancona "Politecnica delle Marche"), Francesco Valentini (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

if (FALSE) {
# 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, Ttol=10)
summary(out)
}