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- W3186276935 abstract "This research uses a Dynamic Panel Data Model (DPDM) to study how unobserved individual heterogeneity affects parameters of inference. The DPDM presumes correlation between lagged dependent variable and individual (unit) specific effects, resulting in heterogeneity among the units. The parameters of DPDM are usually estimated using the classical approach which has no control for heterogeneity of the error terms leading to non-consistent estimates of the parameters. Therefore, Hierarchical Bayesian Estimation (HBE) of unobserved individual heterogeneity of dynamic panel models was used in order to improve on a static panel model even for a panel with small, moderate, and large N. Three experiments for the individual (N) and time (T) were considered: (10, 15), (20, 20), and (100, 15). Theoretical findings are accompanied by extensive Markov Chain Monte Carlo experiments, which show that the estimator performs well and handled the complicated pattern exhibited by the data. The results show closed estimates of the parameters as the dimension of N>T. The performance of the HBE was assessed using Numerical Standard Error (NSE) as N>T produced the least NSE." @default.
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- W3186276935 date "2021-04-30" @default.
- W3186276935 modified "2023-09-25" @default.
- W3186276935 title "Hierarchical Bayesian Estimation of Random Co-efficient Dynamic Panel Data Model" @default.
- W3186276935 doi "https://doi.org/10.24940/theijst/2021/v9/i4/st2104-007" @default.
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