quasi_sym_equ {cquad}

Recursive computation of the conditional likelihood for the Modified Quadratic Exponential Model proposed in Bartolucci et al. (2018)

Description

Recursively compute the denominator of the individual conditional likelihood function for the Modified Quadratic Exponential Model recursively, adapted from Krailo & Pike (1984).

Usage

quasi_sym_equ(eta,s,y0=NULL)

Arguments

eta

individual vector of products between covariate and parameters

s

total score of the individual

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 of 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., Nigro, V., & Pigini, C. (2018). Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model. Econometric Reviews, 37(1), 61-88.

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.