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Fitting Dynamic Regression Models for Panel Data Using Maximum Likelihood and Bayesian Methods
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Sakineh Sadeghi   , Iraj Kazemi * |
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Abstract: (24564 Views) |
| Recently, dynamic panel data models are comprehensively used in social and economic studies. In fitting these models, a lagged response is incorrectly considered as an explanatory variable. This ad-hoc assumption produces unreliable results when using conventional estimation approaches. A principle issue in the analysis of panel data is to take into account the variability of experimental individual effects. These effects are usually assumed fixed in many studies, because of computational complexity. In this paper, we assume random individual effects to handle such variability and then compare the results with fixed effects. Furthermore, we obtain the model parameter estimates by implementing the maximum likelihood and Gibbs sampling methods. We also fit these models on a data set which contains assets and liabilities of banks in Iran. |
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| Keywords: Gibbs Sampling, Initial Conditions, Marginal Likelihood, Random Effects, Variance Copmonents. |
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Full-Text [PDF 439 kb]
(5304 Downloads)
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Type of Study: Research |
Subject:
Statistical Inference
Received: 2011/07/4 | Accepted: 2013/08/13 | Published: 2020/02/18
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