Сравнение методов

	Bayesian Dynamic Panel Data Model	Байесовский анализ панельных данных
Область	Эконометрика	Эконометрика
Семейство	Regression model	Regression model
Год появления	2002–2007	1971–1999
Автор метода	Hsiao, Pesaran, Tahmiscioglu; Arellano & Bonhomme	Zellner (1971); Hsiao, Pesaran, and Tahmiscioglu (1999)
Тип	Bayesian panel model	Bayesian estimation for panel data
Основополагающий источник	Hsiao, C., Pesaran, M. H., & Tahmiscioglu, A. K. (2002). Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods. Journal of Econometrics, 109(1), 107–150.	Hsiao, C. (2003). Analysis of Panel Data (2nd ed.). Cambridge University Press. ISBN: 978-0521522717
Другие названия	Bayesian DPD model, Bayesian lagged dependent variable panel model, Bayesian autoregressive panel model, B-DPD	Bayesian panel model, Bayesian longitudinal model, hierarchical panel model, Bayesian multilevel panel
Связанные	6	5
Сводка	The Bayesian dynamic panel data model extends standard dynamic panel models — which include a lagged dependent variable to capture state dependence — by estimating all parameters within a Bayesian framework. Prior distributions are combined with the likelihood to yield a full posterior distribution over model parameters, enabling probabilistic inference and coherent uncertainty quantification even in short panels.	Bayesian panel data analysis applies Bayesian inference to models with repeated observations on multiple units. By placing prior distributions on coefficients and variance components, it merges prior knowledge with the observed panel likelihood to produce full posterior distributions for fixed or random effects, slope heterogeneity, and variance parameters — rather than point estimates and asymptotic standard errors.