Abstract
We consider regression models for panel data, where regression effects and within subject dependence are allowed to vary over time. We adopt a Bayesian approach with priors that allow shrinkage to constant and zero effects as well as to simpler dependence structures. The model is evaluated in a simulation study and applied to the analysis of yearly earnings of mothers in Austria who returned to the labour market after maternity leave.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bitto, A., Frühwirth-Schnatter, S.: Achieving shrinkage in a time-varying parameter model framework. J. Econom. 210, 75–97 (2019). https://doi.org/10.1016/j.jeconom.2018.11.006
Frühwirth-Schnatter, S., Wagner, H.: Stochastic model specification search for Gaussian and partially Non-Gaussian state space models. J. Econom. 154, 85–100 (2010). https://doi.org/10.1016/j.jeconom.2009.07.003
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pfeiler, R., Wagner, H. (2024). Shrinkage in a Bayesian Panel Data Model with Time-Varying Coefficients. In: Einbeck, J., Maeng, H., Ogundimu, E., Perrakis, K. (eds) Developments in Statistical Modelling. IWSM 2024. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-65723-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-65723-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-65722-1
Online ISBN: 978-3-031-65723-8
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)