2020
DOI: 10.1111/anzs.12316
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Bayesian estimation and model comparison for linear dynamic panel models with missing values

Abstract: Panel data are collected over several time periods for the same units and hence allow for modelling both latent heterogeneity and dynamics. Since in a dynamic setup, the dependent variable also appears as an explanatory variable in later periods, missing values lead to substantial loss of information and the possibility of inefficient estimation. For linear dynamic panel models with fixed or random effects, we suggest a Bayesian approach to deal with missing values. The Gibbs sampling scheme providing a sample… Show more

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Cited by 3 publications
(2 citation statements)
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“…involved in Bayes factors to allow for non-nested model comparison is possible along the lines suggested by Chib (1995), Chib and Jeliazkov (2001) and Aßmann and Preising (2020) in the context of linear dynamic panel models.…”
Section: Cor(θmentioning
confidence: 99%
“…involved in Bayes factors to allow for non-nested model comparison is possible along the lines suggested by Chib (1995), Chib and Jeliazkov (2001) and Aßmann and Preising (2020) in the context of linear dynamic panel models.…”
Section: Cor(θmentioning
confidence: 99%
“…There are generally two ways to deal with missing values [ 17 ]: When the continuous deletion period of the feature sequence is too long or the number of missing values reaches more than 50% of the total period length, the feature sequence is directly removed. When there are scattered missing values, an interpolation method is used to make up.…”
Section: Research On Runoff Driving Factor Mining Based On Big Data Analysismentioning
confidence: 99%