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Approximate Bayesian Quantile Regression for Panel Data

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Advances in Complex Data Modeling and Computational Methods in Statistics

Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

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Abstract

We consider a novel approximate Bayesian approach to quantile regression in the presence of panel data, based on the notion of substitution likelihood, introduced by Jeffreys (Theory of Probability, 3rd edn., Clarendon Press, Oxford, 1961) and popularized by Lavine (Biometrika 82:220–222, 1995) and Dunson and Taylor (J Nonparametr Stat 17:385–400, 2005). We provide a sufficient condition which automatically guarantees the property of non crossing condition among conditional quantile functions. Posterior computations are performed via an Adaptive Markov Chain Monte Carlo (AMCMC) algorithm which allows to handle a large number of parameters. The performance of the proposed approach is evaluated with simulated data sets.

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Author information

Authors and Affiliations

  1. ACEA ENERGIA SPA, Rome, Italy

    Antonio Pulcini

  2. MEMOETEF, Sapienza Università di Roma, Viale del Castro Laurenziano 9, Rome, 00161, Italy

    Brunero Liseo

Authors
  1. Antonio Pulcini
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  2. Brunero Liseo
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Correspondence to Brunero Liseo .

Editor information

Editors and Affiliations

  1. Dipartimento di Matematica, Politecnico di Milano, Milano, Milano, Italy

    Anna Maria Paganoni

  2. Dipartimento di Matematica, Politecnico di Milano, Milano, Milano, Italy

    Piercesare Secchi

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Pulcini, A., Liseo, B. (2015). Approximate Bayesian Quantile Regression for Panel Data. In: Paganoni, A., Secchi, P. (eds) Advances in Complex Data Modeling and Computational Methods in Statistics. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-11149-0_12

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