Bayesian analysis of dynamic panel data by penalized quantile regression

Ali Aghamohammadi ()
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Ali Aghamohammadi: University of Zanjan

Statistical Methods & Applications, 2018, vol. 27, issue 1, No 5, 108 pages

Abstract: Abstract Existing literature on quantile regression for panel data models with individual effects advocates the application of penalization to reduce the dynamic panel bias and increase the efficiency of the estimators. In this paper, we consider penalized quantile regression for dynamic panel data with random effects from a Bayesian perspective, where the penalty involves an adaptive Lasso shrinkage of the random effects. We also address the role of initial conditions in dynamic panel data models, emphasizing joint modeling of start-up and subsequent responses. For posterior inference, an efficient Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of a real data set, we assess the performance of the proposed Bayesian method.

Keywords: Bayesian quantile regression; Dynamic panel; Shrinkage; Penalized regression (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10260-017-0392-0

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