The purpose of this paper is to introduce the Baysian generalized dynamic models of non-parametric data.
Following a Generalized Dynamic Bayesian statistical inference Paradigm, in this paper, we provide a new approach for multiparameters logistic regression when we have a nonlinear model between and . In this approach Response Variable and the coefficients of the model are depended of time t.
The proposed procedure is illustrated by applying a data set for unemployment in Iran, which has previously been analyzed by various authors.
Eskandari, F. & Naghizade, S. (2005). Bayesian Analysis of Generalized Dynamic linear Models and its Application in Iranian Unemployment Problem. Iranian Journal of Economic Research, 7(23), 165–192.
MLA
Eskandari, F., & Naghizade, S. "Bayesian Analysis of Generalized Dynamic linear Models and its Application in Iranian Unemployment Problem", Iranian Journal of Economic Research, 7, 23, 2005, 165–192.
HARVARD
Eskandari, F., & Naghizade, S. (2005). 'Bayesian Analysis of Generalized Dynamic linear Models and its Application in Iranian Unemployment Problem', Iranian Journal of Economic Research, 7(23), pp. 165–192.
CHICAGO
Eskandari, F. & Naghizade, S., "Bayesian Analysis of Generalized Dynamic linear Models and its Application in Iranian Unemployment Problem." Iranian Journal of Economic Research, 7 23 (2005): 165–192,
VANCOUVER
Eskandari F., Naghizade S. Bayesian Analysis of Generalized Dynamic linear Models and its Application in Iranian Unemployment Problem. Iranian Journal of Economic Research. 2005;7(23):165–192 (In Persian).
