cquad_pseudo | R Documentation |
Estimate the dynamic logit model for binary longitudinal data by the pseudo conditional maximum likelihood method proposed by Bartolucci & Nigro (2012).
cquad_pseudo(id, yv, X = NULL, be = NULL, w = rep(1,n), Ttol=10)
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) |
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 |
Francesco Bartolucci (University of Perugia), Claudia Pigini (University of Ancona "Politecnica delle Marche"), Francesco Valentini (University of Ancona "Politecnica delle Marche")
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.
## 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, Ttol=10) summary(out) ## End(Not run)
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