quasi_sym_pseudo: Recursive computation of pseudo conditional maximum...

In cquad: Conditional Maximum Likelihood for Quadratic Exponential Models for Binary Panel Data

Recursive computation of pseudo conditional maximum likelihood method proposed by Bartolucci & Nigro (2012).

Description

Recursively compute the denominator of the individual conditional likelihood function for the pseudo conditional maximum likelihood method proposed by Bartolucci & Nigro (2012) recursively, adapted from Krailo & Pike (1984).

Usage

quasi_sym_pseudo(eta,qi,s,y0=NULL)

Arguments

eta	individual vector of products between covariate and parameters
s	total score of the individual
qi	Vector of quantities from first step estimation
y0	Individual initial observation for dynamic models

Value

f	value of the denominator
d1	first derivative of the recursive function
dl1	a component of the score function
D2	second derivative of the recursive function
Dl2	a component for the Hessian matrix

Author(s)

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.

Bartolucci, F., Valentini. F., & Pigini, C. (2021), Recursive Computation of the Conditional Probability Function of the Quadratic Exponential Model for Binary Panel Data, Computational Economics, https://doi.org/10.1007/s10614-021-10218-2.

Krailo, M. D., & Pike, M. C. (1984). Algorithm AS 196: conditional multivariate logistic analysis of stratified case-control studies, Journal of the Royal Statistical Society. Series C (Applied Statistics), 33(1), 95-103.

cquad documentation built on March 7, 2023, 6:15 p.m.