R: Conditional maximum likelihood estimation of the basic...
cquad_basic
R Documentation
Conditional maximum likelihood estimation of the basic quadratic exponential model
Description
Fit by conditional maximum likelihood a simplified version of the model for binary logitudinal data proposed by Bartolucci & Nigro (2010); see also Cox (1972).
Usage
cquad_basic(id, yv, X = NULL, be = NULL, w = rep(1, n), dyn = FALSE)
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)
w
vector of weights (optional)
dyn
TRUE if in the dynamic version; FALSE for the static version (by default)
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
ser
robust standard errors
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, pp. 719-733.
Cox, D. R. (1972), The Analysis of multivariate binary data, Applied Statistics, 21, 113-120.
Examples
# 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)
# static model
out1 = cquad_basic(id,yv,X)
summary(out1)
# dynamic model
out2 = cquad_basic(id,yv,X,dyn=TRUE)
summary(out2)