Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/32301
Appears in Collections:Management, Work and Organisation Journal Articles
Peer Review Status: Refereed
Title: From Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM)
Author(s): Zyphur, Michael J
Hamaker, Ellen L
Tay, Louis
Voelkle, Manuel
Preacher, Kristopher J
Zhang, Zhen
Allison, Paul D
Pierides, Dean C
Koval, Peter
Diener, Edward F
Contact Email: d.c.pierides@stir.ac.uk
Keywords: panel data model
Granger causality (VAR)
Bayesian
shrinkage estimation
small-variance priors
Issue Date: 2021
Date Deposited: 19-Feb-2021
Citation: Zyphur MJ, Hamaker EL, Tay L, Voelkle M, Preacher KJ, Zhang Z, Allison PD, Pierides DC, Koval P & Diener EF (2021) From Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models (GCLM). Frontiers in Psychology, 12, Art. No.: 612251. https://doi.org/10.3389/fpsyg.2021.612251
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 income → SWB effect that is not supported with maximum likelihood (ML). This is useful because, second, these priors increase model parsimony and the stability of estimates (keeping them within more reasonable bounds) and thus improve out-of-sample predictions and interpretability, which means estimated effect should also be more trustworthy than under ML. Third, these priors allow estimating otherwise under-identified models under ML, allowing higher-order lagged effects and time-varying parameters that are otherwise impossible to estimate using observed data alone. In conclusion we note some of the responsibilities that come with the use of priors which, departing from typical commentaries on their scientific applications, we describe as involving reflection on how best to apply modeling tools to address matters of worldly concern.
DOI Link: 10.3389/fpsyg.2021.612251
Rights: © 2021 Zyphur, Hamaker, Tay, Voelkle, Preacher, Zhang, Allison, Pierides, Koval and Diener. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY - https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Licence URL(s): http://creativecommons.org/licenses/by/4.0/

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