Journal article

From Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM)

Michael J Zyphur, Ellen L Hamaker, Louis Tay, Manuel Voelkle, Kristopher J Preacher, Zhen Zhang, Paul D Allison, Dean C Pierides, Peter Koval, Edward F Diener

Frontiers in Psychology | FRONTIERS MEDIA SA | Published : 2021

Abstract

This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities (i.e., priors) in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative "shrinkage" or "small variance" priors (including so-called "Minnesota priors") while extending prior work on the general cross-lagged panel model (GCLM). Using a panel dataset of national income and subjective well-being (SWB) we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or toward each other for time-varying parameters, which lends additional support for an..

View full abstract

Grants

Awarded by Australian Research Council's Future Fellowship Scheme


Funding Acknowledgements

This research was supported by Australian Research Council's Future Fellowship Scheme (project FT140100629).